Your Reliability Engineering Professional Development Site
A qualitative and systematic analysis method intended to identify, prioritize, and reduce technical risks of failures to an acceptable level, with emphasis on improving product design or manufacturing process. FMEA is a structured process used to identify and prioritize potential failure modes that should receive an appropriate level of mitigation (elimination, reduction, or prevention).
Failure Modes and Effect Analysis (FMEA) is an excellent foundation for Reliability Programs. It is also a stepping stone to build Reliability, Availability, and Maintainability (RAM) models. Here lies the “beauty” of doing Reliability and Maintenance analysis. Whereby information from one analysis can help enhance another analysis.
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As engineering, quality, and product design professionals, we constantly strive to ensure that our products and processes meet the highest standards of reliability and quality. In our pursuit of excellence, one indispensable tool in our arsenal is Failure Mode and Effects Analysis (FMEA). FMEA offers a structured approach to risk management, enabling us to proactively anticipate potential failures, assess their impact, and prioritize mitigation efforts accordingly. However, the practical implementation of FMEA can sometimes prove challenging. How do we navigate its complexities to derive actionable insights and make informed decisions? Let’s explore some of the key components and best practices of FMEA.
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“The important thing is not to stop questioning.” Albert Einstein
I always encourage readers to ask questions, and include a link in my article to make it easy. Asking questions is a great way to learn.
A reader recently asked, “When performing an FMEA, should the effect consider the mitigated case or unmitigated case? In this article we will look at different aspects that can be considered in order to answer the question.
An “Inside FMEA” reader recently asked a question on the subject of Process FMEAs, specifically whether assigning the cause as “operator error” is useful. This article will share insights into the nature of a cause in Process FMEA, and whether describing cause as “operator error” adds value.
Fred and I podcasted on this topic. Link to the podcast is here.
I’ve seen literally hundreds of Process FMEAs that list “operator error” as the cause of a process related failure mode. Let me be as clear as I can. Assigning “operator error” as the cause in a Process FMEA does not add value. I’ll explain. [Read more…]
[Last month I mentioned that the next article would be on the subject of the application of models in the FMEA process. I am postponing that important topic, in order to do more research. Stay tuned . . .]
This month, I want to discuss one of the most common problems that FMEA teams face: getting confused about the difference between failure modes, effects and causes.
“Things are not always as they seem; the first appearance deceives many.” Phaedrus
What is the difference between FMEA and FMECA? Are they the same or different? The answer may surprise you. Let’s explore this topic.
Some defense-related applications require a special type of criticality analysis, called Quantitative Criticality Analysis to supplement FMEA applications. This is the “C” in what is called FMECA: Failure Mode, Effects and Criticality Analysis. I’ll shorten Criticality Analysis to CA in this article.
What is Quantitative CA? When and why it is used? Can Quantitative Criticality Analysis be used in commercial applications?
Did you know that early FMEA standards did not include recommendations to reduce risk? They limited the analysis to the technical risk, without making specific recommendations. The first time I am aware of that an FMEA standard added a column called “Recommended Actions” was in 1993. Thankfully, it is common practice today to include Recommended Actions in FMEAs.
But what makes for excellent Recommended Actions and what is their role in an FMEA? We’ll begin with the fundamentals.
One of the more challenging aspects of FMEA is the subject of Occurrence. Part of every FMEA standard or procedure is the assessment of Occurrence based on an agreed-upon scale. Here are some pointers for when and how to use occurrence in an FMEA.
We’ll start with a definition. The Oxford English dictionary defines “occurrence” as “the fact or frequency of something happening.”
I am often asked about the application of detection in FMEAs. When and how to assess for the risk of detection can be confusing. Here are some pointers for when and how to use detection in an FMEA.
I am often asked for my opinion about the FMEA Handbook that was jointly published by AIAG and VDA in 2019. Here is a summary of my candid views on this handbook, excerpted from a presentation I gave at the 2019 Guangbin Yang Reliability Symposium.
When designing equipment and processes, engineers leave a safety margin that ensures equipment remains functional when a fault or defect is affecting it partially or wholly. Minor defects affecting production assets should not cause immediate breakdowns. A fault-tolerant system remains operational for predetermined intervals before undertaking corrective measures. Faults affecting the operation of different systems emanate from more than a single source. [Read more…]
“If no one ever took risks, Michelangelo would have painted the Sistine floor.” – Neil Simon
FMEA and Risk Management are two distinct bodies of knowledge. This article explores some of the interactions between these two important methods. It is not intended to be a complete analysis of the similarities, differences, and linkages between FMEA and risk management. Rather, it is a high-level overview.
At first glance, a computerized maintenance management system (CMMS) and Failure Modes And Effects Analysis (FMEA) may appear to be worlds apart. But take a deeper dive, and it’s easy to see that they both share a common component: data. The core of any CMMS is defined by gigabytes of data that guarantee its usefulness and functionality, while the success of the systematic steps in FMEA depends on good quality data.
