Failure Modes and Effects Analysis, FMEA risk methodology

Failure Modes and Effects Analysis, FMEA risk methodology

Jan 15th, 2015

Failure Modes and Effects Analysis, FMEA risk methodology risk matrix

In our day to day practice we frequently see risk assessments methodologies stretched to «fit the clients’ case». In other words, users select improper tools to deliver results. This is a brief summary for the Failure Modes and Effects Analysis (FMEA) risk matrix one of the most common risk assessments methodologies. In the coming weeks we will review several of these methodologies and will end the review with a comparison.

FMEA/PIG matrice 4x4: are the "not likely"/"most unlikely" event with extreme consequences IDENTICAL to an "expected"/"will occur" event with negligible consequences risk?

FMEA/PIG risk matrix 4×4: colours may be misleading. A “not likely”/”most unlikely” event with extreme consequences (bottom row, extreme right) shows up IDENTICAL (yellow) to an “expected”/”will occur” event with negligible consequences risk (top row, extreme left).

FMEA was one of the first systematic techniques for preventative failure analysis. It allowed to study problems that might arise from malfunctions of military systems (1950). It involves reviewing as many components, assemblies, and subsystems as possible to identify potential failure modes, and their causes and effects (consequences). FMEA identifies failure modes, not hazards (malfunctions), like HAZOP does. That’s a subtle, but significant philosophical shift. Failure modes and effects analysis constitute today the backbone of Risk Assessment common practices.

How does a FMEA start?

FMEA starts with an event, a failure, due to an hazard. However, but it does not require a detailed identification of all the possible hazards (like HAZOP) . Subsequently it evaluates failures‘ effects, often following simplified methodologies as described below. FMEA does not explicitly require a detailed understanding/modelling of the systems’ functional relationships.

In FMEA a failure probability can only be estimated or reduced by understanding its mechanism. Therefore if the system is not well understood or an inexperienced reviewer starts the exercise, it is very likely that some failure mode will be left-out. Inter-dependencies are generally neglected, unless a specific effort is made to include cascading events (domino effects). FMEA generally give a false sense of precision and simplicity of risk matters to their users.

Common practice FMEA generally do not include detailed consequences’ analyses. The risk is not properly calculated (oftentimes underestimated). It is common for example, when applying FMEA, to see teams selecting the worst among financial, human, or environmental category of consequences and forgetting their possible combinations.

FMEA results display

Probability Impact Graphs (PIGs risk matrix) often display the results. PIGs 4×4 or 5×5 risk matrix cells coloring gives a sense for risk criticality. Many interpretative problems afflict PIGs. Events with low p, high C  are very different than those with high p, low C. PIGS will however prioritize then similarly (See Figure above).

FMEAs require time. For the curious readers here are two extreme examples: 

  •  A group of 3 experts performed a FMEA for a very specific pipeline over the course of 1 week. At the other side of the spectrum 
  • Three workshops were necessary for the FMEA of a large complex containment facility. They were 3, 3 and 2 days in length with an attendance of 23, 29 and 12 people respectively .

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Category: Consequences, Hazard, Risk analysis, Risk management, Uncategorized

3 responses to “Failure Modes and Effects Analysis, FMEA risk methodology”

  1. Kai Neumann says:

    Nice article. That is the very reason why those who do FMEA and DRBFM not just mandatorily tend to use iMODELER to look at interconnections, soft factors and dynamics as well.

  2. Mosharaf says:

    Products risk assessment fmea

  3. Bruce Wayne says:

    The best thing i liked about is that It involves reviewing as many components, assemblies, and subsystems as possible to identify potential failure modes

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