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Different ways and resources to determine the preventive maintenance (PM) schedule for equipment, particularly rotary equipment.
It has been observed that failure often occurs during development stages.
Kindly refrain from raising your voice! Could you provide more details? I view ferris wheels as a form of rotating machinery, as stated in the article at http://en.wikipedia.org/wiki/Ferris_wheel. However, using it as a substitute for my ventilator, which my wife regularly cleans during scheduled maintenance, is not advisable.
Thank you, Josh. In my opinion, using data is just one method for this task. However, what should be done when determining PM frequencies in instances where past data is lacking (such as setting frequencies for the first time in a new facility)?
Hello svanels, both of the equipments you referenced are rotary equipment. The question now arises: is there a distinction in the resources that can be utilized to specify the frequency for these two pieces of equipment, or do they have their own unique resources? For instance, one possible resource could be the Failure Development period or the MTBF, both of which should be determined from historical data. However, if there is a published standard, the resources may differ. This implies that there could be different standards for each equipment.
Refer to the manufacturer's manual for guidance. If not available, consider starting your equipment once a month and adjusting the frequency based on the rate of deterioration.
In determining the PM schedule for rotary equipment, the equipment manufacturer's maintenance recommendations are an excellent starting point. However, it's also crucial to consider variables such as the environment in which the equipment operates, usage frequency, and historical data on past breakdowns or malfunctions. Software solutions like CMMS (Computerized Maintenance Management System) can also help automate scheduling based on these parameters. And remember, the goal is not just to prevent breakdowns, but to maximize equipment longevity and efficiency.
There are quite a few ways to determine the PM schedule for equipment like rotary systems. Beyond consulting the manufacturer's guidelines, you could employ predictive maintenance (PdM) technologies, such as vibration analysis, thermal imaging, or oil analysis. These can signal potential equipment issues before they become significant problems. Furthermore, actual historical data from your equipment should be highly considered. Over time, patterns may emerge showing when wear and tear typically require maintenance. It's like understanding the "rhythm" of your equipment and scheduling PM accordingly. Remember, consistent and informed monitoring is always key in maintaining machines' health.
One good starting point for determining a PM schedule for rotary equipment could be the original equipment manufacturer's (OEM) manual or recommendations. However, keep in mind that these are generalized and often conservative estimates, which might not completely align with your specific usage patterns and conditions. Integrating data obtained from real-time equipment monitoring tools, such as vibration analysis and thermogenic imaging, can provide invaluable tailormade insights into the equipment's actual wear-and-tear rates. Also participating in industry-specific forums and networking with seasoned professionals could provide more practical and real-world insights into maintenance scheduling. And finally, predictive maintenance models and machine learning algorithms could provide a more systematic and optimized maintenance approach.
One effective way to establish a preventive maintenance schedule for rotary equipment is to integrate usage data and performance metrics using IoT sensors. These sensors can provide real-time insights into operating conditions, helping you identify wear patterns and optimize maintenance intervals based on actual equipment health rather than just manufacturer recommendations. Additionally, tapping into historical maintenance logs and industry benchmarking can further refine your schedule, ensuring it’s tailored to your specific operational needs.
When it comes to setting up a preventive maintenance schedule for rotary equipment, utilizing a mix of manufacturer recommendations and historical performance data can be incredibly helpful. Don't overlook things like vibration analysis and thermal imaging; these tools can provide insights into potential issues that might not be obvious. Leveraging a CMMS (Computerized Maintenance Management System) to track maintenance tasks and equipment performance over time can also help you refine your PM schedule, ensuring it evolves based on real-world use rather than just a one-size-fits-all approach.
Analysis techniques, experience with comparable machinery, manufacturer guidelines from manuals and technical resources, as well as handbooks on Plant Maintenance and Mechanical Engineering are essential resources for determining the best preventive maintenance frequency, regardless of equipment type.
Inquiring about plant maintenance handbooks for maintenance or mechanical engineering, Eugene? Can you provide a specific example or type of handbook you're looking for? Give us more details to better assist you in finding the right resource.
To ensure optimal performance and longevity of a new plant or equipment, it is advisable to conduct a Reliability Centered Maintenance (RCM) analysis. This will help in identifying preventive maintenance tasks, with the original equipment manufacturer (OEM) serving as a valuable reference point. Next, assess the criticality of the equipment based on engineering specifications, process flow, and potential business impact. Categorize the equipment into Critical, Moderate, and Low categories accordingly. Critical equipment should undergo monthly preventive maintenance, while Moderate equipment may require maintenance ranging from monthly to quarterly. Low priority equipment can be maintained on a quarterly to semi-annual basis. It is important to review and adjust the maintenance program after a year by analyzing Mean Time Between Repairs (MTBR) and Mean Time Between Failures (MTBF) to ensure efficiency and effectiveness.
While there may not be a specific handbook dedicated to maintaining the equipment model 123XYZ produced by ACME Co., a comprehensive guide can still provide valuable information on important components such as bearings, motors, variable frequency drives, couplings, and lubrication systems. For detailed instructions on servicing the 123XYZ unit, it is recommended to consult the manufacturer's resources such as manuals or engineers. Some useful handbooks for maintenance reference include the Standard Handbook of Plant Engineering by Robert C. Rosaler, the Maintenance Engineering Handbook by Lindley R. Higgins, and Mark's Standard Handbook for Mechanical Engineers – all published by Mc Graw Hill Co. Additional editorial companies to explore for related materials include John Wiley & Sons, Industrial Press Inc., and Prentice-Hall. For a wide selection of technical publications, visit their websites.
Let's stir up some well-known authors in the mix: Heinz Bloch, Joel Lewitt, Terry Wireman, Keith Mobley, and Vee Narayan, all respected contributors to Plant Engineering publications by Industrial Press Inc.
When determining preventive maintenance (PM) frequencies, it is important to consider the factors leading to equipment failure or potential failure. While time-based PMs are necessary, they only cover a small portion of PM activities. The majority of PM tasks should be based on monitoring the overall health of the equipment. To delve further into this topic, it is essential to grasp the concept of the PF Curve and PF Interval. For a more in-depth discussion on this subject, I have recently written an article for Plant Services Magazine. You can access the article here: http://www.plantservices.com/articles/2006/136.html. I trust that this article will provide valuable insights.
One common question that arises is how to create the Pf curve for essential equipment 010X001 using the available maintenance history data.
The discussion also touched upon the introduction of new equipment. When dealing with new equipment lacking historical data, the question arises: How much data is necessary to create the Performance Function curve? Is one year of data sufficient for analysis?
If you have a history of equipment failures, it can be helpful in determining ways to prevent or predict future issues through methods such as visual inspection and vibration analysis. By understanding potential failure modes and utilizing the knowledge of operators and maintenance personnel, you can identify the Potential Failure (PF) Interval on the curve, which is the timeframe between detectable degradation and functional failure. This information can help you schedule maintenance outages before equipment failure occurs. Even without equipment history, you can still follow the same process by relying on operator knowledge and manufacturer specifications. It is beneficial to familiarize yourself with concepts like Reliability-Centered Maintenance (RCM) and Failure Mode and Effects Analysis (FMEA) to improve your understanding of equipment failures. A helpful resource is an upcoming article in Plant Services Magazine discussing the use of FMEA in designing preventive maintenance programs. For further details, please refer to the attached PowerPoint slides illustrating the PF Curve and PF Interval.
Dear Eugene, Apologies for not addressing your second query earlier. In order to determine the PF Interval for new equipment, I recommend utilizing manufacturer data and collaborating with other companies using the same equipment to gather failure information. Implementing Root Cause Failure Analysis (RCFA) can also be beneficial in this process. I once faced a challenging situation with a hydraulic pump that was causing significant issues for both myself and my company. It took several months and numerous failures across different production lines in multiple countries to gather enough data and identify the PF Interval. By monitoring the health of the pump through flow, pressure, and oil samples, we were able to pinpoint the point on the PF Curve where these pumps would typically fail (at 6.8 GPM). This shift in approach resulted in significant cost savings for us. Although it was a tough few months, as both the pump and production equipment manufacturers were unable to provide adequate information due to the new technology, the end result was well worth the effort. I refrained from sharing our detection methods with them as they were keen on passing it to our competitors. This experience taught me valuable lessons and I'm sure you can relate to similar situations. Ricky
Hello Ricky, I found your slides and notes to be quite informative. However, there is still a lingering question that needs to be addressed: What method do you use to schedule inspection intervals for detecting the critical P moment? Looking forward to your response. Thanks, Rui
In the realm of new technology, manufacturers often rely on feedback from end users to enhance their products. However, there can be concerns about sharing too much information, especially if it may benefit competitors. This dilemma was highlighted by a forum member who worked in equipment manufacturing and emphasized the importance of learning from clients' experiences to improve product quality. While some may worry about sharing knowledge with manufacturers, others see the value in collaborating to receive the latest recommendations and insights. In a fast-paced technological landscape, staying informed and exchanging experiences can help establish effective maintenance programs from the outset. Share your thoughts on this topic or consider starting a new discussion to explore it further. Your input is valuable.
Hello everyone! I may be new to this thread, but I want to share my thoughts on on-condition maintenance, as defined in the SAE standard on RCM. The main goal is to detect the "P" moment, which is when a potential failure can be identified using chosen technology. Once the P-F interval is established, the frequency of the task must be less than this interval. There are different opinions on this, with some suggesting a task frequency of 1/3 of the P-F Interval, while others believe 50% is best, especially for safety-related failures. The interval should be adjusted over time based on more accurate P-F estimates. Modern RCM, as outlined in SAE JA1011, recognizes four types of routine maintenance and three types of corrective maintenance, as well as reactive maintenance. Within routine maintenance, there are specific categories such as Predictive Maintenance, Preventive Restoration, Preventive Replacement, and Detective Maintenance. Only Preventive Restoration and Preventive Replacement are considered as "PM's" within RCM. Each type of maintenance determines the optimal task frequency differently, based on factors like the P-F interval, asset life, and acceptable probability of failure in a given situation. These concepts have evolved from outdated thinking that prevailed two decades ago.
Different viewpoints exist on the optimal task frequency for condition-based maintenance intervals. Some military experts suggest a frequency of 1/3 of the P-F Interval, citing the need to account for potential inaccuracies in failure detection. This approach results in increased routine maintenance activity, with three checks conducted within each P-F interval. On the other hand, an alternative perspective argues for a frequency of 50%. Personally, I lean towards this approach, except in cases where failure poses safety or environmental risks. I have engaged in debates regarding the rationale behind factoring in condition-based task intervals. Conducting on-condition tasks three times within the PF interval could significantly inflate maintenance costs. This increase may not be necessary if a conservative estimate of PF is initially considered, especially when uncertainty is present. While adjustments can be made to account for less robust inspection tasks, it is crucial to address the underlying issues causing their inefficiency or explore alternative predictive methods. The origins of these rules may be akin to old wives' tales, as sarcastically suggested by workshop attendees likening them to messages found on restroom walls that suddenly become policy.
Hey Steve, I agree with you on this point. I've heard similar views, especially from military experts. The consensus is that inspections may not always detect potential failures. One example of this is relying on human senses for condition monitoring. Through my experience, I find that technology is more effective at detecting warning signs of failure.
Steve, when you mentioned the concept of fixed inspection intervals at 50% or 33% of the P-F interval, it reminded me of a common saying that some may dismiss as an old-wives' tale. As one participant at our workshop humorously phrased it, "around here, someone writes something on the dunny wall, and before you know it, it becomes policy." In case you were wondering about the origin of these rules, I provide an explanation in my book on pages 190 and 191. While I'm currently away from my usual resources, I believe Nolan & Heap also cover this principle in their work. Let's just say I may qualify as an honorary "old wife" based on this knowledge.
Thank you for your feedback, Vee. I am unable to locate my copy of Nowlan and Heap as well. It seems that I may have lent it to someone and forgotten to retrieve it. I am eager to find a solid answer on this issue, so I hope you can help. From what I remember, this particular information was not included in Nowlan and Heap. However, I would appreciate it if it was, as it would provide a clear source. On a side note, could you please send me a complimentary signed copy of your book?
Steve, I just want to clarify because I'm a bit confused. Are we on the same page that the frequency of routine tasks in a Predictive Maintenance (PdM) task should be less than the P-F Interval? It seems like we agree that around 50% is adequate for identifying most, if not all, failures. Now, the question is whether intervals like 33% or other shorter or longer periods are justifiable. Can we confirm if this is correct?
Quote: It seems that we can agree that around 50% is adequate for most, if not all, failures, right? Daryl, I beg to differ on this point. I suggest that the key purpose of the interval is to allow sufficient time to fix a fault after detection without disrupting operations significantly. This aspect is separate from the PF interval, so incorporating PF into the interval calculation deviates from the logical process and imposes an illogical rule. Quote: Therefore, the essential question is whether 33% or other shorter or longer periods are justifiable. Am I correct in saying this? I propose that the focus should not be on a fixed factor like 50%/30% or any specific percentage. Instead, estimate the order of magnitude (days, weeks, 1 month, 2 months, etc.) of the PF (err on the side of caution if accurate pinpointing is challenging). Then, determine the duration required for planners to implement a correction and choose an interval for inspection that minimizes production disruptions. It would not be wise to select half of the PF interval as the inspection interval if the lead time for parts is 70% of PF. Regards, Steve
It's great to hear from you, Darryl! Feel free to reach out to me at ricky.smith@ivara.com to continue our conversation on the PF interval. Although most experts suggest taking half of the PF interval, studies show that time-based failures only make up 20-30% of total failures. As a former DS Maintenance Company Commander in the US Army Reserve handling equipment for over 100 coalition units in Kuwait and Iraq, I observed that time-based PMs were not effective during extreme conditions like sandstorms, heatwaves, and floods. Equipment failures seemed to be primarily random, leading the US Military to adopt the 1/3 rule for a more cautious approach. The decision to err on the "safe side" is driven by risk and consequences, which ultimately impact labor and material costs. As touched on by Darryl, RCM defines four types of routine maintenance: PdM, PS, PR, and DM. This topic is complex, highlighting the importance of maintenance professionals having a solid understanding of failures and how to anticipate or prevent them. For a visual representation of these four types of routine maintenance, feel free to reach out via email and I'll be happy to provide you with a graphical illustration.
How to accurately determine the scale of the performance curve for equipment such as turbines, compressors, and pumps? Are there any specific guidelines to follow? Additionally, how can you ensure timely delivery of spare parts for repairs? Here are some valuable tips for creating an effective maintenance strategy: 1) Create a comprehensive equipment bill of materials (BOM) with accurate material master data and inventory management. For critical equipment like standalone compressors, maintain capitalized spare parts, and for essential equipment like pumps, establish min-max stock levels. 2) Establish a pricing agreement with a spare parts supplier to enable immediate delivery upon request. 3) Consider setting up a service agreement with a vendor for overhauls, if not performed in-house, to reduce lead time for maintenance work. Combining items 2 and 3 into a package contract can help streamline contracting activities and optimize efficiency.
Seeking to accurately estimate the magnitude of the PF curve for equipment like turbines, compressors, and pumps? Wondering if there are any guidelines to follow? According to Josh, the key is consulting the experts and asking the right questions. In the realm of industrial equipment, the answers may not always be found in a manual or digital resource. Regards, Steve.
The US Military employs the 1/3 rule to prioritize safety and mitigate risks. This rule is driven by the balance between risk assessment and its associated consequences. Factors such as labor and material costs are key components in determining the "safe side" approach. In industrial settings, budget limitations often dictate decision-making processes.
Spencer Hatfield is spot on when he states that many organizations lack sufficient equipment history and neglect to calculate the P-F interval. Without accurate MTBFs and machine history, reliability programs will inevitably fail to reach their full potential. To overcome this challenge, Spencer suggests starting with 3db or 0.707 of the P-F interval and then refining the approach with data. The key to success lies in obtaining and utilizing accurate data, as this is often the reason why reliability programs fall short. However, some companies do understand this concept and strive for world-class performance. By incorporating automation into CMMS and work order systems to gather the necessary data, these programs can achieve even greater success. Regards, Spencer Hatfield.
When it comes to improving reliability programs, having accurate data is key. Without it, our goals may be harder to reach. It's important to start with a solid foundation, such as using a percentage of the P-F interval as a starting point, and then fine-tune using collected data. In my experience, this method has been successful. It's also worth noting that it can be challenging to obtain accurate failure data in maintenance, as the ultimate goal is to prevent failures altogether. This emphasizes the importance of the type of data being collected, particularly failure data. How do you utilize your data to optimize your reliability programs?
Hello Daryl, I hope you are well. My apologies for the delay in responding to your post from the 17th. Let's delve deeper into this topic... Many maintenance professionals often choose to follow a set time interval between inspections, usually a fraction of the PF (Potential Failure) interval. I recently came across a paper by Steve Turner discussing the differences between Cost Minimization Algorithms and the RCM concepts introduced by Nowlan and Heap in 1978. However, I have some reservations about his approach. Firstly, in my opinion, it doesn't seem logical to stick to a constant time interval when dealing with potential degradation failure modes. Ideally, the interval should decrease progressively as the likelihood of failure increases with time. Secondly, if the repercussions are solely operational and not a matter of safety or environmental concern, why not take a calculated risk of not detecting the failure in time and facing the consequences? If the issue is purely financial, the costs of dealing with failures may not outweigh the expenses incurred in conducting frequent inspections. There might be a cost-effective inspection schedule that minimizes expenses over time. Do you agree? For instance, let's consider a scenario where a mechanical device mainly fails due to wear and tear described by a Weibull distribution with specific parameters. The accumulated lifespan, previous inspection details, and the required reliability level all play a crucial role in determining the optimal inspection intervals. In practice, these intervals may need to be adjusted for practicality. It is clear that shorter intervals are necessary as time progresses. I can provide an example involving the PF interval in my next post. I would greatly appreciate your insights on this matter. Best regards, Rui
Do you know who the best experts to consult are on this matter? Should we reach out to operators, consultants, or OEMs? And what specific questions should we be asking? Can you provide examples to help clarify our approach?
Implementing a strategy of gradually reducing inspection intervals appears to be a practical approach. By enhancing the frequency of inspections once signs of failure are detected, we can accurately gauge the rate of deterioration. This proactive method can help businesses optimize maintenance practices and prevent costly breakdowns.
Rui, who do you anticipate will reply to your post - Daryl or me?
According to Josh, the concept of gradually reducing inspection intervals appears plausible. By increasing inspection frequency upon detecting signs of failure, we can more accurately assess the rate of deterioration. This adjustment should only be made once failure has been confirmed.
I agree with Josh and Steve regarding the potential identification of failure. However, your argument suggests that we should strive to reduce this timeframe, potentially leading to a situation where we monitor the equipment constantly to prevent failure as predicted.
Steve, could you provide insight on "how to approach and what to inquire about" when estimating the PF curve? I purposely didn't direct this question solely at you to invite input from others in this public platform. When I seek information from operators, they sometimes hesitate to cooperate due to fears of potential repercussions. Is this a common occurrence during RCA exercises? What specific questions should I ask to obtain accurate information and foster open communication? Would asking, "What is the average machine failure rate?" prompt operators to redirect me to CMMS?
quote: Steve, I trust you have insights on "Who to consult and what to inquire about?" when estimating the PF curve. I purposely didn't direct my question solely to you to encourage others to contribute in this open forum. Josh – If you are interested in a condition-based maintenance program, seek guidance from individuals with expertise in equipment and failure characteristics. These individuals could be operators on a packaging line or even a university PHD who developed a structural model for a large mast on a crane or dragline. There are various methods to determine the appropriate inspection interval. Asking "What is the PF interval?" is not the most effective approach. Instead, consider providing training on task selection concepts so that those being consulted understand the rationale behind the questions. Emphasize the importance of preventing unexpected failures and ensure that the maintenance tasks are robustly designed with intervals that prevent failures from occurring unnoticed. The key question then becomes "How frequently should this equipment be inspected for a specific failure mode to ensure it is always detected?" Prompt individuals to consider different inspection intervals, such as every four weeks or two weeks, to determine the most suitable frequency. For issues like leaks with variable PF, highlight the possibility of a leak existing for the entire inspection interval if it occurs shortly after the last inspection. In this case, ask "How often would you be willing to tolerate a leak in that area without anyone noticing?" That's a good starting point for you, Josh. Your inquiry is akin to asking a soccer player how to shoot a ball - there are multiple strategies to achieve success, with experience and skill playing a crucial role in the outcome. This underscores the value of quality training and coaching in maximizing effectiveness.
Hey everyone, I'm currently swamped with work but I will make it a priority to address these points in the next day or so. This thread is fantastic! Rui mentioned the importance of adopting a constant time interval to detect warning signs of failure, rather than focusing on degradation failure modes. If we are dealing with degradation failure modes like wear on pumps or rail tracks, a different approach may be needed, such as refurbishment based on calculable life or Weibull analysis. Steve emphasized the importance of considering the time needed for planners to implement corrective actions when determining inspection intervals to minimize production disruptions. It's crucial to align inspection intervals with plant requirements, production processes, and safety/environmental needs, rather than solely focusing on inventory lead times. If the lead time for a part is longer than the timeframe for inspection, it's essential to have at least one on hand to avoid delays. I will revisit this discussion soon, apologies for the brief response. (Hi Ricky, I'll reach out to you shortly, hope everything is going well for you.)
Hello Daryl, I want to discuss the importance of reliability in plant operations. While lead time for inventory items may not directly impact reliability, it is essential to consider factors like plant design, maintenance, and spare parts management. These factors play a crucial role in ensuring the availability of equipment and minimizing downtime. Maintenance policies should be tailored to the specific needs of the plant, considering the cost of spares, inspection, and downtime recovery. Sometimes, it may be more cost-effective to stock spares on site, while in other cases, frequent inspections may be sufficient. In situations where lead time for spares is a concern, like in military exercises or remote locations, increasing the rate of inspection can help mitigate risks. It's important to understand the unique requirements of the plant and make informed decisions about maintenance strategies. I appreciate your insights on this topic, Daryl. Let's continue to explore ways to improve reliability in plant operations. Thank you. Regards, Steve
In the realm of plant maintenance and reliability, it's essential to focus on the specific requirements of the production process and safety standards rather than just the lead time of inventory items. It is crucial for maintenance personnel to evaluate the inherent reliability of equipment under its operating conditions and make decisions based on that assessment. Starting with a predetermined inventory value and trying to adapt it to the equipment and operating context can lead to ineffective maintenance practices. As mentioned by Steve and Daryl, prioritizing the needs of the plant and safety regulations is key to ensuring optimal reliability and efficiency.
Hello Steve, I agree with the main points of our discussion, but it seems we are getting caught up in minor semantic details in other areas. However, I believe it's important to address these nuances because they play a crucial role in the overall discussion. The reliability of a plant is influenced by its design and operational management. While you emphasize the importance of maintaining the plant for availability, I believe that reliability is a combination of design, operational requirements, and management strategies. It's not solely dependent on how the plant is operated and constructed, as there are multiple factors at play. When it comes to maintenance, various factors drive decision-making, including the cost and capacity of spare parts, inspection costs, downtime recovery, and more. While holding spare parts was once common practice, many companies are now shifting towards vendor-managed stock and shared risk models. This shift allows for more flexibility and cost-effective inventory management. In some cases, the cost of holding spare parts may outweigh the benefits, leading companies to opt for frequent inspections and purchasing parts as needed. However, economic decisions should also consider the impact of asset non-availability on plant operations. For high-cost items like tires in the mining industry, the cost of downtime far outweighs the cost of holding spare parts. In conclusion, effective stores management is crucial for maintaining plant reliability and availability. It's important to strike a balance between holding necessary spare parts and efficiently managing lead times. Making informed decisions based on the specific needs of each plant is essential for a successful maintenance strategy.
Hello Daryl, let's discuss the importance of plant reliability. It's a combination of both design and how we operate the plant. Operating a machine a certain way is different from simply requiring certain things from it. The way we operate the machine affects its reliability, as it is based on its design and operation. Smart management plays a crucial role in maintaining the machine's health through regular checks and necessary modifications. This helps improve the apparent reliability of the machine post-maintenance. However, the fundamental reliability is determined by factors such as design and operating conditions. It's a topic open for discussion - perhaps other members of this forum can share their thoughts. Regards, Steve.
In Topic 2, the discussion revolves around the impact of holding stock on conducting predictive maintenance tasks within a company. Contrary to a particular quote, I believe that the decision not to hold stock is often influenced by the frequency of inspections and the ability to procure parts in a timely manner when needed. This highlights the importance of efficient inventory management in ensuring smooth operations.
In Topic 3, the discussion revolves around the economic implications of rejecting purchases based on their impact on asset availability. While expensive items like tires may seem cost-prohibitive, the potential downtime costs make it a necessary investment. When lead times are a concern, it may be wise to factor in a certain level of downtime in inventory management. The cost of not having a critical part can have significant financial repercussions for a business, making it essential to consider these factors in decision-making processes. Cheers, the economic considerations in such decisions are crucial for the overall success of the business. My apologies if terminology was unclear before. Steve.
Dear Rui, Although you initially requested a response from Daryl, as your post delves into the topic of the paper I authored and Daryl has not yet provided a reply, I felt compelled to weigh in. Firstly, it is illogical to adhere to a consistent time interval when anticipating a degradation failure mode. Rather, the interval should progressively decrease as the likelihood of failure increases over time. It is commonly agreed upon that as observable degradation occurs, the frequency of inspections should be heightened. Secondly, if the potential consequences are primarily operational and do not involve safety or environmental risks, it may be acceptable to assume a certain level of risk in failing to detect the progression of a failure in a timely manner. In cases where the issue is solely monetary, the repercussions may not outweigh the costs of continuous inspections. It may be deemed worthwhile to take the chance. Therefore, from a purely economical standpoint, there could be an optimal inspection calendar that minimizes costs over a designated period. Do you agree? While your insights are valid, Rui, from a mathematical standpoint, the scenario you propose does not align with my calculations. A condition-based task is either cost-effective or not. For some tasks, there may be no economic justification for any inspection interval. For tasks that are economically viable, the mathematically optimal interval, according to my calculations, aligns with the point of failure (PF) itself. However, this assumes that the necessary rectification action can be taken instantly. Furthermore, my calculations also assume that inspections are 100% reliable, which is not always the case. In situations where inspection reliability is not guaranteed, a cost curve can be derived to determine the minimum cost based on varying levels of inspection reliability. In conclusion, the paper I authored on cost minimization algorithms delves into these assumptions. While these algorithms have been widely embraced in the engineering field, some are flawed and make questionable assumptions. I welcome your feedback, as these are engineer-based calculations rather than purely mathematical. Warm regards, Steve
Hello Steve, instead of repeating ourselves, I'd like to summarize where I believe our conversation has led us. It's important to avoid a back-and-forth argument that doesn't really contribute on forums. I have consistently emphasized that the reliability of an asset is determined by its design and our requirements for it. My point, which I've mentioned before, is that our operational and maintenance strategies are influenced by our needs for the asset. This is a commonly accepted concept. While I understand your concern about economic factors, your initial statement suggested something else. So, I addressed what I thought you were trying to convey. It's great to see that we are on the same page with this. To elaborate on my original point: If a part is expensive but the inspection process is cost-effective and non-intrusive, then it may not be worth keeping the part in the inventory from an economic standpoint. I want to emphasize that in a scenario where a maintenance task needs to be done every two weeks based on RCM criteria, it is essential to have the necessary parts available in a timely manner. Whether it's through vendor managed inventory, consignment stock, on-site storage, or any other method, having access to the parts is crucial. The reliability of a plant is not solely dependent on immediate part availability, as there are various modern solutions for ensuring parts availability. I also don't entirely agree with the idea of decreasing inspection intervals, but that's a discussion for another time.
Thank you, Daryl, for your input. As it seems, the discussion on this topic has come to an end. I am keen to engage with Rui if he is still interested in continuing the conversation. I value hearing different perspectives, especially ones that contrast with my own. Looking forward to hearing from Rui. Regards, Steve.
Hello, I recently took a break in the countryside without any technology, which I believe is important for everyone to do once in a while. I apologize for not responding to some questions earlier. As an engineer who frequently advises managers in uncertain environments, I find that quantitative methods are crucial in decision-making processes. The topics under discussion, such as scheduling inspections to prevent failures and managing spare parts inventory, can be objectively analyzed from an economic standpoint. I have conducted extensive research on these subjects, including developing algorithms that have been successfully implemented in industrial settings. Attached are two original documents in Portuguese that explain my mathematical approach to these issues. Let me briefly describe one case discussed in the papers: The PF interval, which involves predicting and preventing mechanical failures in equipment. I have used analytical methods and Monte Carlo simulations to determine the optimal reliability between inspections (0.91 in this case). The software I developed takes into account various factors to optimize inspection schedules. Another topic addressed is the decision of whether to keep spare parts in stock. By analyzing factors such as equipment life expectancy, failure rates, and costs, I have found that in some cases, it is more cost-effective to purchase parts only when needed rather than keeping them in stock. My goal is to demonstrate the importance of utilizing quantitative methods in maintenance practices. Thank you for your understanding. Regards, Rui
Thank you, Rui, for the challenge that will put my math skills to the test. I'll need some time to rise to the occasion. It's great to hear that you took a break – I could use one myself. Best regards, Steve.
Hello Steve, I apologize for the error in sending the wrong version of the document "Chapter II". Please download the correct version titled "Inspections and Spare_1" (I have also updated it in my previous post). Thank you. Attached is the file "Inspections_and_Spare_1.zip" (250 KB, 1 version).
In the applicability criteria of SAE 1012 diagram 17, the initial set of questions involves determining the presence and consistency of a P-F Interval. It is important to note that using an RCM approach may not be conducive to diminishing frequencies caused by diminishing P-F intervals. For further reference, the standard likely contains information regarding these questions. Cheers, Rui / Steve.
Hello Daryl, According to SAE 1012 (page 30, point 13.1.c), it is recommended that the task interval should be shorter than the shortest potential PF interval. The decision diagram (page 49, figure 17) also mentions the PF interval as you mentioned in your previous post. However, I believe that SAE 1012 is merely a suggestion rather than a mandatory requirement. I personally do not agree with its guidelines on PF interval. I thoroughly contemplated this subject a few years ago and came up with the approach outlined in my previous post. While some colleagues may have differing opinions, I am open-minded and welcome their explanations based on solid mathematical principles. Adhering strictly to rules may impede progress, so it is essential to question their validity in light of evolving contexts and advancements in knowledge. Do you share the same viewpoint? Rui
Hey Rui! I completely agree with your points. Firstly, it's important to note that SAE 1012 serves as a helpful reference guide rather than a strict standard. Is there any specific information within the standard regarding P-F interval setting? Secondly, it's crucial to constantly question and improve current practices. However, I have reservations about the effectiveness of the diminishing P-F strategy you mentioned. I'm happy to provide more insights on this topic, but I won't be able to do so today. Apologies for the delay, mate!
Hey everyone - In my previous statement about decreasing PF intervals, the condition was that the intervals should be decreased after significant deterioration is observed. Let's consider condition monitoring using a specific example - if a crack is found in a structure, the rate of crack growth will increase based on the crack's length. This implies that the PF is decreasing over time, presenting two options: increase inspection frequency or repair the crack before it leads to a catastrophic failure. It's important to note that all airplanes have cracks, but not all of them have been repaired. This may indicate that my previous explanation was not sufficiently clear. Regarding the consistency of PF intervals - leaks, for instance, vary in their degradation rate. However, most maintenance analyses I've conducted include leak checks. Neglecting these checks could result in losing clients and eventually going out of business. I haven't had the opportunity to review Rui's formulas yet, but if they suggest reducing inspection rates as equipment ages, I would like to understand the reasoning behind it. Many experts following Nowlan and Heap's approach believe that age and deterioration rates are not directly correlated in most cases. While the PF may decrease for some failure modes in older equipment, the deterioration rate typically remains within the same order of magnitude (such as from weeks to less than a day).
Steve, I haven't had a chance to read your previous message yet, but I wanted to address the main topic. Sorry for any misunderstanding. I will make sure to revisit this soon.
In the model I developed, it is important to clarify that the decreasing intervals of PF remain valid until a failure is detected, even if the time periods are shorter than PM. PM represents the minimum time available to react and prevent a functional failure, known as safe time windows. When a potential failure is identified through inspection, the equipment will be promptly halted within an estimated timeframe before reaching the critical point F. At that point, the faulty component will be either repaired or replaced. Sincerely, Rui.
Hello Rui, I am seeking clarification on your suggestion. Are you proposing a reduction in the interval between inspections either prior to or following the detection of a possible failure (point P)?
Hello Josh, as equipment ages, the likelihood of degradation failure modes such as erosion increases. It is essential to adjust the frequency of inspections accordingly to ensure optimal performance. When a failure is detected, the decision to halt operations or continue depends on understanding the Preventive Maintenance (PM) interval. Additional inspections may be necessary to monitor the rate of failure progression. If the failure is unpredictable, inspection intervals should remain constant. However, for early failure modes, inspection intervals should gradually increase. Regards, Rui.
Rui, there is a widespread belief that challenges the assumption you mentioned. Many individuals believe that the probability of failure and the rate of decay are typically seen as unrelated in most industrial settings, with the exception of items that begin to deteriorate from the moment they are put into operation. It is commonly understood that the frequency of inspections should be increased as the likelihood of a degradation failure mode, such as erosion, rises with age. It is logical to infer that the time between inspections should decrease gradually to prevent such failures. In the occurrence of a failure, the condition starts to worsen from that point onward. Is there any existing evidence that supports the idea that deterioration rates are dependent on the age of the material? For instance, would a crack in a wing spar grow noticeably faster if it initiated at 5000 hours compared to if it started at 50,000 hours?
Please find attached an EXCEL file that demonstrates the principles behind my analysis on the economics of inspections for obvious failures. This method is often discussed in reliability literature. The analysis does not consider the PF interval but instead focuses on the PMF (PM + MF) interval interaction. Feel free to review the attached file for more details. Regards, Rui Attachment(s): Inspections.zip 6 KB 1 version.
Rui, your arguments are truly thought-provoking, pushing me to delve deeper into the subject matter. While I may risk getting criticized, I embrace the opportunity to learn and grow from this discussion. I propose that utilizing the Weibull distribution can provide a probability of failure at any given time, leading to the identification of various risks. However, I argue that Probability of Failure (PF) is not directly correlated with the risk of failure, except in rare cases where decay initiation points and rates are known. The rarity of such instances is evident in my experience, prompting the question of the relationship between risk of failure and inspection intervals. If inspections are 100% effective, only one inspection within the PF timeframe is necessary to detect failure before it occurs every time. The cost-effective inspection interval is therefore equal to the PF, adjusted for recovery time. The decision on inspection intervals hinges on cost-effectiveness calculations, comparing the cost of inspections to the cost of unexpected failures. It is crucial to consider second-order effects, such as lead time to repair and the potential for missed faults during inspections. Striving for high-confidence inspections is essential, with some tasks considered fail-safe. While some inspections may overlook faults, methods exist to model and justify additional inspections within the PF timeframe. The Weibull parameters representing the risk of failure come into play in such scenarios. The data you presented on June 25, 2006, outlines key factors like the predominant failure mode, accumulated life time, last inspection, and costs associated with inspections and failures. While there may not be data on inspection variability, PF variance, or lead time variance, the optimal inspection interval is suggested to be at 450 hours, regardless of the current risk of failure. As an engineer, I acknowledge that my interpretation may be subject to debate and scrutiny, but I stand by my analysis and conclusions.
After reviewing your Excel work, Rui, I have noticed similarities to previous scenarios that raise similar concerns.
Hello Steve, I apologize for my silence recently. After reflecting on your feedback, I revisited the case and discovered that costs are actually lower when inspection intervals remain constant. Surprisingly, the least cost is achieved when the inspection interval matches the PM interval (PF = PM + MF). Initially, I overlooked the concept of constant inspection intervals as I was steadfast in my belief in the variable time intervals approach. However, I now realize the importance of this oversight and am currently conducting further tests to identify the variables that have the most significant impact. In the meantime, I have attached an Excel file for your reference. The "Picture" sheet visually represents decreasing time intervals and "safe" time windows, while the "Costs" sheet includes some test results. I am working towards drawing solid conclusions and appreciate the opportunity this discussion has provided for reevaluating my assumptions. Thank you. Rui Attachment(s) Test_results.zip 6 KB 1 version
Hey Rui, hope you're doing well! We need more people like you in this industry, my friend. Your unique perspective and attitude are truly valuable.
Rui, as Daryl has highlighted in his previous response, understanding the key variables with the greatest impact is crucial. I am eager to see your conclusions once you complete your review. Regards, Steve from OMCS International.
I apologize for the delay in response, Steve. I am back at headquarters now. Please provide me with a physical mailing address so I can send you a copy of my book. You can reach me via email at eml@effective-maintenance.com. Thank you for your patience.
Thank you, Vee, for recommending this fantastic book. I can't wait to receive the signed copy. Best regards, Steve from OMCS International.
Rui, I'm having trouble opening the Test results file in Excel. Is this issue affecting others as well?
I successfully managed to get it working properly.
I apologize for my prolonged silence despite my promise to return with my conclusions. I highly value all of you and aim to contribute by showcasing scientific methodologies that can enhance the competitiveness of maintenance practitioners. In exchange, I look forward to learning from your knowledge and experience. Unfortunately, my schedule has been packed with university exams and project bids, leaving me with no time to delve into this topic in depth. I plan to return within a week. Feel free to visit my website, launched two weeks ago, at http://www.rassis.com for articles and Excel tools on Operations, Productivity, Reliability, Performance, and Statistics. I personally designed the site to be as user-friendly as possible. I hope you find it engaging. Wishing you all success in your work. Rui
I found it challenging to comprehend the mathematical concepts presented in the papers due to a lack of explanatory language. However, I am proficient in interpreting spreadsheets. Is there a software available for translating Portuguese text into English? Translating these papers into English would undoubtedly broaden their reach to a larger audience.
Hi Josh, thank you for your interest in the papers on the site. To help with translations, you can use BABELFISH, a translation tool available at http://babelfish.altavista.com/. In the future, I may create an English idiom version. I will also provide a brief explanation of the mathematical calculations involved in determining inspection intervals, along with the results of my ongoing research on this topic. Stay tuned for updates next week. Regards, Rui.
Hello Josh, I've recently discovered that certain documents on my website are password-protected. If there is a specific document that you require a translated version of, please inform me so I can send you an unrestricted copy. Best regards, Rui.
"Looking for a Portuguese to English translation software? Look no further than online dictionaries such as FreeDict, IDP Search, and Lookwayup for all your translation needs from Portuguese to English."
Finally, something in Portuguese to savor, as Brazilian and European Portuguese have their distinctions. Great job, Rui!
For further information on that topic, refer to the following link: http://www.deltatranslator.com/dtr3.htm. This resource may provide additional insights on the subject at hand.
Rui, let's review the two articles attached on page 3 titled "Inspection & spares.zip" and see how they can benefit us.
Hi Josh, Thank you for your interest. I am providing you with the same two documents that were attached on page 3 of this thread. This time, they are both unprotected, making it easier to translate them into English. Please note that the document titled "CapÃtulo II" is currently being revised based on our recent discussion about inspection intervals, but the mathematical content remains accurate. I will also be unprotecting all PDF documents on my site. Regards, Rui Attachment(s): Inspections___spares_2.zip (245 KB) - 1 version
Steve, you mentioned that when an inspection is 100% effective, only one inspection is needed within the PF. However, the challenge lies in accessing the PF in the first place, as the starting point (P point) is usually unpredictable. If the P point is known, the F point can typically be estimated. To guarantee entry into the PF interval, it has been demonstrated that inspecting at 50% or less of the PF interval always ensures entry. This principle can be likened to a common old wives' tale, showcasing the importance of strategic inspection methods.
Hello Vee, I believe that P and F are unpredictable variables with varying means and variance. When establishing condition-based intervals, our goal is to estimate the rate of degradation to better plan inspection frequency. This allows us to avoid unexpected failures and address faults with minimal disruption. One interesting concept is that inspecting at 50% or less of the PF interval always keeps us within the desired range. This principle may seem uncertain, but in a normal distribution scenario, with a mean of 1 month and two standard deviations covering 95% of the population, a deviation of one week can lead to 2.5% falling below two weeks. I'm open to understanding your mathematical perspective on this. Thank you. Best regards, Steve
Steve, as you mentioned, "When we establish condition-based intervals, we are essentially estimating the extent of deterioration." You are absolutely correct in your assessment. One method to assess the degradation rate is by determining the P and F points. Continuous monitoring would provide this information, but unfortunately, this isn't always feasible. The key question then becomes what to do in such situations. Setting the appropriate condition monitoring interval allows you to detect a failure post-P but before F. For more in-depth information, refer to pages 64-68 of my book. It is crucial to note that the PF curve is a physical degradation model, not a statistical one. This delineates two distinct yet interconnected fields. While some may overcomplicate it with statistical analysis, the PF curve doesn't necessitate sophisticated statistical techniques. For metrics like vibration levels or delta pressure, absolute measurements are essential, not predictive values. When dealing with failure patterns D, E, and F, which are statistical hazard plots, decision-making can be challenging. The question arises - why schedule preventive maintenance on a specific day when the hazard rate remains consistent over a range of days? In such scenarios, the physical degradation model of the PF curve takes precedence over statistical methodologies. By identifying points on the PF curve, we can predict the location of the F point and plan repairs accordingly. I dispatched the book over a week ago, so you should have received it by now. If not, please let me know, and I can assist in tracking its delivery.
Understanding the common failure patterns of HVAC systems can help you identify issues early and prevent costly repairs. What are the most frequent issues that occur in HVAC systems?
Hello Vee, I recently received your fantastic book while I was out of town. I managed to read the introduction and glance through a few chapters on my first night back. I am eager to dive deeper into it when I have some free time. Thank you, Steve.
In order to enter the PF interval successfully, it can be demonstrated that inspecting at 50% or less of the PF interval will always result in successfully entering the PF interval. This concept is reminiscent of a well-known anecdote. I am intrigued by your statement, Steve Vee, and am curious about how you arrived at this conclusion. If we consider that PF follows a normal distribution, I am interested in how you can establish that inspecting at half the PF will ensure that the rate of decay will never exceed half of PF. I am taking some time to ponder the other points you made in relation to linking hazard rate and degradation. It seems to me that they may be mutually exclusive in practical terms. I believe I need to reflect further on this before responding - and also review your text on the topic. Best regards, Steve
When it comes to HVAC system failures, the pattern is similar to a bathtub model, just like with your car. Breaking down the HVAC into components and failure modes reveals different patterns, similar to examining your car and focusing on the tires as a singular set. Tires typically experience three main failure modes: punctures, leaking valves, and wear. If punctures and leaks occur randomly, the tire will show a pattern of random wear. However, if the valve is considered separately, a bathtub curve may be observed. The shapes of these patterns vary depending on the level of analysis, but at the HVAC level, they will ultimately aggregate to a bathtub model.
Have you checked the HVAC fan belt and air filter yet? It's important to take a look at these components to ensure your system is running efficiently and effectively. Regular maintenance of the HVAC fan belt and air filter can prevent costly repairs down the line. Don't forget to inspect these parts regularly to keep your system in top condition.
Steve has gathered a wealth of data on fan belts from various manufacturing plants. Surprisingly, a common pattern that emerges from fan belt data analysis is the "bathtub" pattern. This pattern typically starts with a high rate of infant mortality, followed by issues with alignment and pulley condition that can shorten the belt's lifespan. Eventually, the belts wear out entirely. Some data even shows that the mean time between failures (MTBF) is only about half of the estimated wear life. When it comes to filters, one would expect a wear-out pattern, but in reality, process upsets often lead to premature failure. To combat this, many filters are changed based on condition or duplex filters are used to ensure minimal production downtime during changes. If you're interested in Failure Patterns, Steve posted some discussion and data on this topic in April 2006. If you're studying HVAC, do you have any failure data to share as well? Regards, Josh.
I am eager to explore HVAC failure data, specifically regarding AC motors. Thank you for your insights.
I have the work orders on hand. Can I take a look at your work orders, Steve?
Hello everyone, I have completed all my students' examinations and have written a document discussing the issue of inspections (comparing constant and variable time intervals). In addition to these options, there is also the alternative of "no inspections." The costs of each alternative are dependent on various factors, making it difficult to determine which option - variable time intervals, constant time intervals, or no inspections - is the most cost-effective in any given scenario. Therefore, I recommend thoroughly evaluating all three alternatives when economics play a significant role in decision-making. Best regards, Rui. Please see the attached document for further details.
In the ongoing discussion between Steve and Vee regarding the PF curve, I would like to contribute some additional insights. When addressing failures post point P and uncertain of the exact location of point F, one can utilize statistical methods based on historical data, testing information, physics-of-failure models, or covariate models. Author Charles E. Ebeling's book, "Reliability and Maintainability Engineering" (McGraw-Hill, 1997), provides valuable insights on this topic. In a statistical approach, one can employ a Normal distribution in three steps. Firstly, gather input from individuals familiar with the specific failure mode to estimate pessimistic and optimistic PF intervals, resulting in PF1 and PF2 curves. Secondly, derive the parameters for a Normal distribution based on the mean and standard deviation of PF1 and PF2. Lastly, incorporate this random variable into a simulation model alongside other probability distributions to assess scenarios such as inspection strategy costs. On the other hand, in the physics or covariate realm, consultation with experts in high-temperature equipment, such as steam boilers and turbines, can provide mathematical models for component failure modes based on various factors. By ranking failure times, one can determine the dominant factor and estimate remaining component life. Collaboration with knowledgeable colleagues is crucial in this process. Best regards, Rui
In my previous post, I discussed choosing point F with a specific probability of occurrence earlier than expected. For example, if PF1 = 500 hours and PF2 = 600 hours, the mean would be 550 hours and the standard deviation would be 25 hours. By accepting a 10% risk of overestimating point F, a calculation using EXCEL shows that point F should be approximately 520 hours. I hope this clarifies things. Thank you, Rui.
Rui, you and Ebeling make valid points, but implementing these steps in practice is challenging. Obtaining an accurate estimate of the P-F interval can be difficult, as it is often impossible to pinpoint the exact F point. The P point is not typically documented in CMMS, and the F point is usually unknown until corrective action is taken. This lack of data presents a challenge in using precise mathematical models in real-world scenarios. It is crucial to gather reasonable estimates of the P-F interval to effectively plan and schedule maintenance tasks. Variability in the P-F interval, particularly in terms of days or weeks, poses a significant challenge in predicting when maintenance is needed. The key is to identify the P point in a timely manner to allow for proactive planning before reaching the F point. If the variability in P-F interval is high and difficult to predict, it may not be feasible to establish a confident inspection interval. Drawing from years of experience in maintenance, interacting closely with equipment operators has proven to be an effective way to estimate P-F intervals. However, predicting the exact timing of the P and F points remains a challenge in the field.
Hello Vee, estimating the P-F interval may seem challenging, but it is achievable with the right skills and mathematical tools to guide decision-making. In my previous post, I shared my approach to this task. Having worked with a team of engineers specializing in process plants equipment for industries such as refineries and power generation, I understand the importance of inspections and predicting when equipment failures may occur. By utilizing advanced technology and a careful balance between precision and cost-effectiveness, we are able to estimate when fatigue or corrosion will reach a critical point (F point) after detecting initial signs of deterioration (P point). While historical data and input from operators are valuable resources, there are times when engineering estimates based on failure models are essential for solving new challenges. Although it can be tough when data is scarce, relying on experience and intuition becomes necessary. Fortunately, I rarely encounter such situations. I hope this information has been helpful. Best regards, Rui.
Rui, there seems to be a misunderstanding regarding my previous statement. I want to clarify that while obtaining an estimate of the P-F interval may not be an easy task, it is certainly achievable. I am confident that we can derive P-F estimates by engaging with operators and maintainers. The real challenge lies in precisely defining the F point, not the P-F interval. By identifying the beginning of failure shortly after the P point, we can predict the likely F point based on the estimated P-F interval.
Rui, you mentioned that you rarely encounter this type of situation, which is fortunate. I have never come across a database containing P-F intervals. This can be attributed to the fact that the operating context differs greatly from one case to another, making it difficult to establish a standard P-F interval. The interval depends heavily on equipment operation and maintenance practices. It seems like you have access to advanced information, but I am skeptical about the universal applicability of your data.
Hello Vee, I want to clarify that the data I mentioned is not universally applicable. This data originates from long-standing equipment or similar equipment operating under comparable conditions. When we detect the beginning of a failure (P point), we estimate the F point by analyzing existing data and statistics, or by applying relevant knowledge of the physics of failure for new cases. Regards, Rui.
When it comes to detecting the onset of a failure (P point) without continuous on-line monitoring, how can we effectively identify it? Understanding the P-F interval plays a crucial role in determining the frequency of monitoring so we can anticipate the P point or shortly thereafter. By doing so, we can proactively plan for repairs before reaching the F point. The key challenge lies in noticing the P point without continuous monitoring, making the variability measure of the P-F interval essential. While variability can be determined through statistical analysis, the lack of on-line monitoring in most scenarios complicates the process. Nonetheless, the P-F interval remains valuable in determining monitoring frequency in order to effectively capture the P point. This highlights the importance of addressing the issue of monitoring frequency, rather than assuming prior knowledge of the P point.
Dear Vee, I apologize for the confusion earlier. In the past, I had access to a database that kept track of instances of failure detection for specific parts and modes over the years. My colleagues analyze the remaining lifespan of these parts based on failure mechanics, considering factors like material condition and working conditions. This information helps advise premises owners on when to replace parts or conduct further inspections. We also calculate the "incubation period of failure" to better understand where potential failures may lie on a timeline. Best regards, Rui
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Answer: 1. What factors should be considered when determining the preventive maintenance schedule for rotary equipment? - When determining the preventive maintenance schedule for rotary equipment, factors such as equipment type, operating conditions, manufacturer recommendations, historical maintenance data, and criticality of the equipment should be considered.
Answer: - Historical maintenance data can be used to identify patterns of equipment failures, predict maintenance needs, and establish a preventive maintenance schedule based on the frequency of past maintenance requirements.
Answer: - Yes, there are various software tools and applications available that can help in scheduling preventive maintenance for rotary equipment by automating maintenance tasks, tracking equipment performance, and generating maintenance alerts based on predefined criteria.
Answer: - Common preventive maintenance tasks for rotary equipment may include lubrication, alignment checks, vibration analysis, bearing inspections, belt tension checks, and overall equipment condition assessments.
Answer: - The frequency of preventive maintenance for rotary equipment can vary based on factors such as equipment type, operating conditions, and manufacturer recommendations. It is important to establish a schedule that balances the need for regular maintenance with the operational requirements of the equipment.
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