For those seeking information on PM optimization, consider reading the article "A data driven path to PM optimization" published in MT, Feb 2011 (p.19-22). The article offers a method for determining inspection intervals based on desired availability and MTBF. It is vital to understand the statistical nature of such calculations, requiring data distribution including mean value and standard deviation if the distribution is normal. However, the author only provides MTBF and desired availability, leaving the formula for calculating failure finding interval (FFI) - inspection interval - questionable. This approach has two major flaws: 1. Failure causes are grouped together when calculating MTBF, making it unlikely to have a distribution. 2. Even if a distribution exists, standard deviation is not addressed. These shortcomings make the FFI formula unreliable and impractical for PM optimization. Instead of being a useful tool, it becomes a misleading academic exercise, diminishing the credibility of the reliability process.
It is important to consider that the assumption underlying the end result of extending frequencies is to ultimately save money. It is essential to differentiate between wear failures and random failures in order to effectively implement preventative maintenance strategies. While random failures may be difficult to predict and prevent, there are established wear failure modes that can be proactively managed through preventive maintenance measures.
Thanks for sharing the article - actually, I read it a while ago and shared some of your concerns. Emphasizing the lack of information on standard deviation hits the nail on the head. By ignoring the variability of data, we could be painting a false picture of system reliability. This would certainly make the FFI formula questionable. However, despite its limits, I still think the method could be a fair starting point, but it should definitely be supplemented with more robust statistical input and risk analysis to give more comprehensive optimization results.
Thanks for sharing your detailed insights on this article. I think your critique about the lack of consideration for standard deviation and grouping failure causes together in the MTBF calculation hits the nail on the head. It's unfortunate to see methodologies being propagated without a holistic view of reliability and maintenance strategies. It does indeed turn a potentially useful tool into somewhat of an abstract academic exercise. Maybe the author could do a follow-up piece addressing these issues to restore some confidence in their method.
I absolutely agree with your analysis. While the approach in the mentioned article tries to present a data-driven method, it essentially oversimplifies the complex nature of determining maintenance intervals. Not separating failure causes when calculating MTBF does indeed compromise the reliability of distribution. And not addressing standard deviation oversimplifies the complexity of realistic data models. It's a reminder for us not to blindly follow methodologies without scrutinizing the underlying assumptions and evaluating their practical applicability.
You've raised some excellent points about the limitations in the article's approach to PM optimization. It's crucial to not only consider MTBF and desired availability but also to factor in the nuances of failure data and its distribution, as this can significantly impact the reliability of inspection intervals. The flaws you mentioned could definitely lead to misleading results, highlighting the need for a more nuanced method that includes a proper analysis of failure types and their individual impacts on the overall maintenance strategy. A comprehensive understanding of both mean and standard deviation would make the FFI calculation much more reliable and practical in real-world applications.
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Answer: - The article provides a method for determining inspection intervals for preventive maintenance optimization based on desired availability and MTBF.
Answer: - The calculations require data distribution, including mean value and standard deviation if the distribution is normal.
Answer: - The approach has two major flaws: failure causes are grouped together when calculating MTBF, making it unlikely to have a distribution, and even if a distribution exists, standard deviation is not addressed.
Answer: - The shortcomings in the formula make it unreliable and impractical for PM optimization, turning it into a misleading academic exercise that diminishes the credibility of the reliability process.
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