In my view, inspections can be predictive only if there is a standardized process in place. Predictive Maintenance (PdM) tests simply need to have measurable, repeatable, and trendable aspects. - Josh
It's important to note the significant impact of failure analysis, which may outweigh the benefits of predictive maintenance (PdM).
- 01-12-2024
- Heather Coleman
It's important to remember that predictive technologies serve a much broader purpose than solely identifying failures. These tools should be utilized to spot underlying issues such as misalignment, inadequate lubrication, or improper installations that could potentially lead to failures. By concentrating solely on identifying failures, we are not maximizing the full potential of these tools. Alan
It is important to understand that the term 'predictive' in reliability maintenance does not refer to predicting the failure itself, but rather predicting the time of functional failure. Predictive Maintenance (PdM) techniques monitor the rate of change in an item to determine when it will no longer be able to perform its function. These techniques do not predict failures, but rather how long we have until the item is no longer functional once the failure process has already begun. Inspections are necessary to verify certain parameters in this process.
On the other hand, failure detection or detective tasks are used to identify items that have already failed without our knowledge. For instance, testing a smoke or gas detector provides a clear yes or no answer about its functionality. In such cases, there is no prediction involved as the failure has either occurred or not. PdM technologies are not effective in these situations as they rely on detecting changes in parameters. Instead, tests are needed to determine whether an item is still operational.
This informative discussion highlights the importance of predictive technologies beyond just failure detection in industrial equipment. By using advanced inspection tools, such as monitoring vibration levels and measuring temperature with IR tools, we can identify issues like piping size problems or set points causing failures. It's crucial to monitor for any deviations from normal operation and analyze them to determine the root cause and severity. This proactive approach allows for timely repairs and extends equipment life. For example, detecting discrepancies in pressure gauges on pumps can help prevent inefficiencies and potential overflows. Predictive maintenance not only ensures smooth equipment operation but also aids in new equipment acceptance and overall installation assessment. By utilizing all our senses and employing predictive strategies, we can address issues early on and ensure optimal performance and safety. Selling the idea of predictive maintenance may be challenging, but with management support, success is attainable. In conclusion, predictive maintenance offers numerous advantages without any notable disadvantages, making it a valuable asset for any industrial operation.
When a machine begins operation, the potential for failure also starts. Despite advancements in designs and materials, there is no machine that is immune to failure. Monitoring deviations from normal functioning is crucial. It is true that all items experience degradation from the moment they start operating. However, every item is engineered with a certain level of tolerance for degradation, known as a margin of safety. For example, in a ball bearing, fatigue loads on the races exist from the start, but it takes time for subsurface fatigue cracks to develop and cause visible damage. This is when vibration readings may start to indicate a problem. Prior to this point, the readings remain constant, much like a flatline on a heart monitor. Once a crack forms on the race, it may eventually lead to a crater, which will cause further damage as the balls roll over it, resulting in increased vibrations. This stage is known as the Potential Failure point (P point) in Reliability-Centered Maintenance (RCM) terms. Predictive Maintenance (PdM) is effective from this point onward because it focuses on monitoring changes. PdM is most effective after the onset of failure, as it indicates that some of the design margin has already been consumed. However, there is usually still some margin left, which will soon be depleted, leading to Functional Failure (F point in RCM terms). PdM can help anticipate this point of functional failure. This is why PdM is used to forecast the timing of failure, rather than the failure itself. Predicting failure itself is the job of statisticians, while predicting the time of failure falls within the realm of physicists.
Hello Rennie, I want to revisit the original question about the disadvantages of using predictive technologies. While I am a strong advocate for the use of predictive maintenance techniques and technologies, I believe there are situations where they can actually be a disadvantage, rather than an advantage. This usually occurs when predictive technologies are overused or misused. Firstly, it is a disadvantage when the use of predictive technologies is not cost-effective. Additionally, it is a disadvantage when they are applied in situations where they are not suitable. The RCM standard can offer guidance on when predictive technologies are appropriate. Lastly, misapplying predictive technologies to manage a failure mode that could have been prevented is also a drawback.
For example, predictive technologies are effective when detecting signs of physical degradation in an asset like a bearing. However, it is important to consider what caused this degradation in the first place. Bearings commonly fail due to factors like metal fatigue, which can be caused by issues such as overloading, overheating, or poor lubrication. These failure modes can often be prevented or mitigated through operational changes, maintenance practices, or design improvements.
Simply relying on predictive maintenance techniques may help predict when a functional failure will occur, but it may not address the underlying factors leading to early failure. In the end, this could result in unnecessary costs and losses. It is essential to consider a holistic approach that includes preventive measures alongside predictive technologies to ensure optimal asset performance and reliability.
I hope this contribution adds depth to the conversation. It is easy to be dazzled by technology, but insights like these can offer valuable perspectives. Thank you for the discussion. Best regards.
Daryl, I appreciate you returning us to our origins. The question remains: What initially triggered this situation? To add to the conversation, poorly installed bearings could be a contributing factor. This raises the issue of proper installation for optimal performance.
Workers lacking proper training are often considered unskilled in their job roles. This can hinder their performance and productivity, leading to potential issues in the workplace. Companies must invest in providing adequate training to ensure all their employees are skilled and proficient in their work.
Hey there, I overlooked several causes of failure in bearings in my previous post. The goal wasn't to list every possible failure cause, but rather to highlight the importance of avoiding misapplications of predictive maintenance. This can result in increased maintenance expenses and decreased uptime. Some other reasons for bearing failures include an unbalanced rotating element, bearings positioned too high on a tapered sleeve in a pump, excessive interference fit between bearing and shaft (often due to shaft thermal expansion), decreased lubrication viscosity from overheating, and the ingress of moisture or particles. There are likely many other failure modes as well. Warm regards,
When considering the disadvantages of implementing new technology (assuming you already know the advantages), certain factors should be taken into account. Firstly, there is a higher cost associated with investing in tools such as a thermocam compared to a more traditional tool like a pipewrench. Secondly, there may be a need for additional training, especially if the current staff is not familiar with the new technology. This could require hiring new employees, potentially causing tension among existing staff members. It is also important to gain support from management, as making promises without being able to deliver results can be detrimental. Additionally, it is crucial to ensure that all team members are willing to learn and adapt to the new technology, as this can be a challenging hurdle to overcome. Focus should be placed on understanding the technology as a whole, rather than just focusing on one aspect, such as bearings in the case of Vibration Analysis. Building a strong foundation is key, as simply investing in a new tool without a clear plan or strategy may not lead to the expected return on investment. It's important to approach these challenges with a positive mindset and a willingness to learn and adapt.
Steven, your post really resonated with me. Especially when you mentioned the need for additional staffing to handle the glamorous work, which can make existing employees feel overlooked. This situation is all too common as companies adopt more predictive technologies. As you rightly mentioned, this can leave other employees feeling disheartened. It's important to remember that while results may be tied to machinery performance, the use of these technologies should involve and respect the people working with them. Best regards,
quote: Vee's statement is accurate – predictive technologies are incredibly valuable in detecting issues before equipment degradation occurs. For example, when a bearing shows signs of physical wear such as cracks in the races, vibrations can alert us to potential problems and allow for timely intervention to prevent catastrophic failure. However, it is a misconception that predictive technologies only signal equipment failure. In reality, they can identify issues like misalignment or soft foot during startup, enabling corrective action to be taken without long-term damage to the equipment. By utilizing these tools to proactively address design flaws and system issues, we can prevent failures that may have gone unnoticed otherwise. It is important to recognize the value of predictive technologies in mitigating risks and maintaining equipment reliability before it is too late.
When it comes to hiring additional staff to handle the glamourous work, it's important to consider how it may make the existing team feel left out. Should these "golden boys" be brought in from outside or should preference be given to in-house employees with maintenance knowledge and a strong track record? Is it fair to automatically award the position to someone who has been with the company for a long time, or should it go to the person who has truly proven themselves capable? While experience is valuable, effort and performance should also be taken into account when making hiring decisions. Share your thoughts and experiences on this topic.
The concept of "golden boys" isn't unique and applies to various tools, such as lathes commonly found in many workshops. Skilled workers and mentors knowledgeable in materials and heat treating are crucial for efficient operations. Imagine a hypothetical factory facing frequent breakdowns and downtime. The idea of purchasing a lathe is considered, but without an operator, the plan falls through. This situation may sound familiar, with experienced individuals pulled in different directions due to budget constraints. In a similar scenario, an old mentor explained the absence of a lathe in his shop, citing a lack of support from management. His perspective sheds light on the challenges of working with limited resources.
It's important for managers to not only consider experience but also effort when awarding jobs. Ensuring that every manager knows their team is crucial for fair opportunities. One effective way to evaluate skills is by observing employees in a classroom setting and gaining insight into their activities outside of work. Unfortunately, many managers don't spend enough time with their team to truly understand their capabilities and potential.
Hello Awicker, I wholeheartedly agree with your comment. It is indeed false to claim that predictive technologies are not effective in identifying equipment issues before they worsen. Take, for example, the scenario of a misaligned motor pump. By utilizing vibration readings during startup, predictive technologies can pinpoint alignment issues early on. In my previous post, I emphasized that misalignment can lead to both initial vibrations and eventual bearing failure. The focus was on how predictive technologies can anticipate failure without addressing the root cause. This is why I referenced Vee's case study on equipment degradation. Best regards,
When installing a motor pump, it is crucial to ensure proper alignment to avoid potential issues. Vibration readings taken during start-up can help identify alignment problems and other issues such as high amplitudes of vibration at electrical frequencies. By correcting the alignment and addressing any soft foot problems, we can prevent bearing damage and motor air gap issues. Using instruments like vibration meters, flow meters, or pressure gauges during installation is essential for quality assurance. These instruments help us confirm that equipment is in proper working condition before handing it over for use. While these measurements do not always predict future issues, they are valuable in assessing the current condition of equipment. It's important to distinguish between using instruments to establish the status of equipment and using them to monitor changes during normal operations, which can help predict future problems. Ultimately, the true value of an instrument lies in how we use it to achieve our goals.
Hello Vee, I completely agree with your point. However, Awicker was emphasizing that the technologies in question can also detect issues beyond physical deterioration. This aspect is crucial when considering the advantages and disadvantages of these technologies. Regards,
Daryl, while I value your perspective, I beg to differ. It's important to note that the instrument itself is not the process; rather, it can be utilized for a range of processes. Predictive Maintenance (PdM) is a distinct and specialized process. However, I do concur with your point that if we invest in equipment such as an infrared camera for PdM purposes, we can also leverage it and our trained personnel for other applications. This flexibility enhances the overall efficiency and value of our resources.
Hello Vee, I'm a bit unclear on why there is a disagreement here. It seems that Awicker was emphasizing how the technologies in question have the capability to address issues beyond just physical deterioration. Your viewpoint seems to align with this statement, "Technologies being used to pick up issues other than physical degradation." Regards,
In larger retail establishments, it is crucial to consider the perspective of having a maintenance team consisting of 8 to 30 individuals. This group is typically divided into area maintenance teams, with around 8 people per area overseen by a maintenance supervisor and area superintendent. It is important for these leaders to have a good understanding of each maintenance person in their respective areas. Providing equal opportunities for all team members is essential, and regular training sessions are typically held for everyone. With a small team, the learning curve is quick, and work ethic is easily observed. It is crucial to pay attention to day-to-day activities as they can impact potential candidates. Stay vigilant and observant in the workplace.