Thank you for your inquiry, Svanels. While I am unsure of the specific conversation thread on this topic, I wanted to share a brief comment following Lucas's remark. It is important to consider factors such as reliability, availability, and costs when determining the effectiveness of a 90-10% pump regimen. When operating with two pumps - one as the duty pump and the other as the standby pump - it is crucial to understand that their mission time is equal. The duty pump operates 90% of the time while the standby pump operates for the remaining 10%, swapping roles when necessary. This system ensures that if one pump fails, the other can easily take its place while repairs are being made. I hope this explanation clarifies any confusion. Best regards, Rui.
In the forum discussion on operating philosophy, we also touched upon the topic of the Best Strategy. Your assertion regarding the mission time being the same for both the main and standby pumps is quite straightforward. While the main pump's primary mission is to function effectively, the standby pump is designated as a backup, regardless of the time. This distinction is crucial in ensuring operational efficiency and preparedness in case of any unforeseen circumstances.
Despite receiving no responses, I will illustrate this with an example: If a pump fails three times in one year and operates for 8760 hours, how can we calculate its reliability without relying on statistical distributions? Does anyone have the answer? (I'm beginning to wonder if there even is one).
Luca, do you have a different perspective on the definition? Rui Assis originally stated, "Reliability is defined as the probability of success for a certain mission." If reliability is based on probability, is it necessary to search for alternative mathematical methods or procedures for its calculation?
- 04-10-2024
- Vanessa Carter
There are alternative methods to assess reliability beyond using a probability distribution formula. One option is to utilize addition, such as adding 9 multiple times to reach a total of 81. However, personally, I find multiplying 9 by 9 to be a more preferable approach.
I am comfortable using statistical distributions, but I am looking for an algebraic formula that is easy for my management to understand. It seems that managers prefer simplicity when it comes to calculations. While exploring further, I discovered that availability, a statistical parameter, can be calculated using the formula MTBF/(MTBF+MTTR). I am curious if there is a similar formula for reliability. Where do managers study anyway? It appears they struggle to comprehend anything beyond basic algebraic calculations like x+-/.
Effective managers are constantly juggling various financial considerations to ensure the smooth operations of their plant and drive profits. They are keenly aware that investment in the plant must yield returns to cover expenses and generate income. Conversely, failing to address maintenance issues promptly can result in significant financial losses. Managers prioritize practical solutions over theoretical discussions, as they require tangible results to keep their operations running smoothly and efficiently. In the fast-paced world of plant management, being prepared for unforeseen situations is key to success.
Luca, maybe it would be more effective to communicate in terms of money rather than using algebra or statistics. Just my two cents. - Mike.
I am interested in finding an algebraic formula for determining reliability. If we use an exponential distribution, the reliability of a one-year mission can be calculated as R=exp(-N) where N represents the number of failures. In the case of N=3, the reliability is 50%. What are your thoughts on this approach?
When considering the example of having 3 failures in a year, it is important to understand the patterns and probabilities at play. For instance, if a component has been failing every 4 months and the last failure was 2 months ago, the likelihood of another failure within the next 3 months is 100%. However, if the failures occur at irregular intervals with no apparent pattern, it may seem unpredictable. Only through investigation can it be revealed that the failures are due to chance occurrences, such as random events like a stone hitting a car's windshield.
By analyzing the time intervals between failures and applying the exponential probability function, a mathematical pattern can be observed. This function, commonly used in various scenarios like waiting lines and system failures, can help determine the probability of success or failure within a specific timeframe. It is important to note that relying solely on a suggested formula may not always be accurate.
In conclusion, understanding the probabilistic nature of failures and utilizing mathematical models like the exponential probability function can aid in predicting and managing reliability. If you have any further questions or need assistance, feel free to reach out. Regards, Rui
- 04-10-2024
- Frances Fisher
In response to Rui's statement, it is important to note that when using the exponential distribution, the probability of success (reliability) or failure can be determined for a specific time period (such as running hours, miles, cycles, etc.). The formula suggested by Rui is incorrect. If we consider an exponential distribution, the reliability for a one-year time mission can be calculated as R=exp(-N), where N represents the number of failures. In our example, with N=3, the reliability would be 50%. To clarify further, the calculation would be: R=exp(-(3/8760)*8760)=exp(-3)=exp(-N). Apologies for any confusion, but it appears there was a misunderstanding in the previous email.
Just kidding, don't be upset. Let's revisit my question about reliability. The reliability factor represents the probability of success. If we analyze historical data to calculate reliability, consider this example with a popular pump that experienced 3 failures in 2005. The reliability can be computed as rel=exp(-3) using an exponential distribution, which is a statistical method. Does this calculation seem accurate to you, or do you have a more effective approach?
It appears that you have a simplified formula that is specific to calculating the mean failure rate over a year for a particular time mission. This formula is valid only for this special case, as it uses the number of failures per year to determine reliability.
Thank you, Eugenio, for your input. If you, Luca, are eager to improve your knowledge and skills in Reliability and Availability (R&A), I suggest conducting a search with "Reliability" as a key term on a search engine. This will lead you to a plethora of valuable resources. Consider exploring an e-book or purchasing an introductory level book on the subject. You can also check out references on popular platforms like Amazon. If you'd like, I can recommend a title that I personally recommend. Best regards, Rui.
My expertise in reliability is backed by over a decade of experience working with the Navy, oil and gas companies, and nuclear plants. While I am well-versed in statistical distribution, I was seeking a more user-friendly approach. Despite this, I have yet to receive a response to my initial question, including my own suggestions, from you, "Rui."
Are you looking for a Maintenance Engineering Manager who can effectively oversee and maintain equipment and plant facilities worth millions of dollars? Our ideal candidate is experienced in applying various scientific and engineering principles to equipment, managing maintenance budgets exceeding $$$ million, and supervising a team of engineers and technical professionals. They should be able to effectively communicate with colleagues from different backgrounds and handle complex mathematical formulas, such as R = exp (- N * t). As someone who learned to use exponential functions in High School with a basic calculator, I believe that those who struggle with this concept may not have the necessary qualifications for a leadership role in maintenance engineering. With my Bachelor's in Industrial Engineering and ongoing pursuit of a Master's degree, I am confident in my abilities to excel as a Director or President in this field.
Perhaps the negative sign is what truly catches the attention of managers and leaves a lasting impact.
- 04-10-2024
- Heather Coleman
Luca, if you possess expertise in reliability, it seems unnecessary for you to keep questioning the matter. Both Eugenio and I have already provided thorough insight on the topic. It is puzzling why you continue to inquire. I have nothing further to add. Please keep your desired response to yourself. I wish you success in your endeavors. Best regards, Rui.
I continue to ask repeatedly because I still do not have a response to my query. If my question is unclear to you, please refrain from offering a response.
Welcome to this online community where professionals generously offer free advice without any monetary compensation. Let's ensure a professional environment by taking a moment to consider our words before posting.
I would like to express my gratitude to Svanel for their valuable advice. The beauty of an open forum lies in the opportunity to share your thoughts or challenges with a wider audience. That is precisely why I have kept my question open - I am still seeking a satisfactory solution and believe that there might be additional insights that others can contribute.
When assessing reliability, one commonly used metric is the mean time between failures (MTBF) of a pump. The MTBF for your pump is calculated to be 2920 hours, indicating the average time it takes for a failure to occur. However, it's important to note that this number doesn't guarantee the pump will fail every 2920 hours, as the actual failure pattern may vary. To delve deeper into this, statistical methods can be employed to analyze the failure distribution. For instance, if failures are assumed to be random, an exponential distribution can be used. Understanding the failure distribution allows us to determine the probability of the equipment functioning as intended at a specific time, expressed as a percentage.
For example, if through effective maintenance the pump experiences fewer failures, decreasing from 3 to 2 per year, the MTBF would increase to 4380 hours. This improvement indicates a higher level of reliability compared to the initial MTBF of 2920 hours. To further understand the likelihood of failure, a detailed statistical analysis is recommended.
Luca, Snap Ring is correct in pointing out the importance of statistics in dealing with uncertainty. When faced with an uncertain situation, statistics provide us with the tools to analyze and make informed decisions. For example, if you have observed 3 failures in a period of 8,760 hours, it is essential to carefully note the time elapsed between each failure. By analyzing this data, we can identify the type of failure and estimate its parameters with confidence intervals.
If we assume that the failures are occurring randomly, we can apply the exponential distribution with a failure rate of 0.000342 failures per hour. Using this information, we can calculate the reliability of a system over a given period of time. For instance, the reliability of a system not experiencing a new failure within 1,000 hours would be 0.710017.
In a scenario where failures occur at regular intervals, such as every four months, the reliability of a system can vary depending on the timing of the last failure. If the next mission is scheduled for one month from now, the reliability would be 0. However, if the mission is delayed to three months, the reliability would be 100%, as the system has proven to be reliable during that timeframe.
In conclusion, statistics play a crucial role in understanding and managing uncertainties in various scenarios. By employing statistical methods, we can make informed decisions and optimize system reliability. If you have any further questions, feel free to ask. Regards, Rui.
Luca, possessing knowledge in both theory and practice, allows you to understand the distinction between deterministic and probabilistic processes. Deterministic processes, such as chemical reactions and dynamics governed by definable equations, yield predictable outcomes. On the other hand, probabilistic processes, like failures and accidents, follow gaming laws and are best analyzed using statistical methods. Attempting to simplify these processes with algebraic or arithmetic expressions is equivalent to forcing a square peg into a round hole. Rui and others are diligently working to address your inquiries, with Rui even developing a simulation model for this purpose. Their efforts deserve recognition, as acknowledged by Eugene and Svanels. However, it may be difficult to fully satisfy your query as you seem inclined to disregard the statistical aspect of probabilistic problems.
- 04-10-2024
- Jasmine Howard
I want to express my gratitude for the valuable assistance provided by everyone. I am aware that probabilistic methods are more suitable for assessing reliability than deterministic ones. I posted the question on the forum seeking opinions from others in the field. The unanimous consensus among the professionals here regarding the absence of a simplified algebraic formula is reassuring. The collective response has boosted my confidence significantly. Thank you to all who contributed to this discussion.