Risk Assessment Techniques

And how we help you improve your system reliability and asset integrity

 

Risk Identification Techniques

There are different techniques for assessing risks which are used to support you in making decisions, such as whether you need to address the risk and how to do that. In other words, these techniques are used to identify, evaluate, and prioritize potential risks to your asset or organization. One of the common risk assessment techniques to identify risk includes Failure Mode and Effects Analysis (FMEA). It is a systematic method that helps organizations identify and prioritize areas where they need to improve, in order to reduce the risk of failure.

To perform FMEA analysis you need to:

  1. subdivide your system, process or a hardware into elements
  2. state the ways each element might fail and potential effects of the failure
  3. record the following information for each element:
  • its function
  • the failure that might occur (failure mode)
  • the mechanisms that could produce these modes of failure
  • the nature of the consequences if failure did occur
  • whether the failure is harmless or damaging
  • how and when the failure can be detected
  • current control in place to compensate for the failure

Failure Modes, Effects, and Criticality Analysis (FMECA) is a risk assessment technique that is similar to FMEA, but it also includes an assessment of the criticality of a failure mode and a more complex prioritization of risks. Criticality refers to the importance or significance of a failure mode, and it is used to prioritize actions and resources to mitigate risk.

Both FMEA and FMECA are powerful tools that can help organizations identify and prioritize risks, and to take action to mitigate those risks before they occur, but FMECA is generally used when the stakes are high, and the failure of the system or process could have severe consequences.

Consequence/Likelihood Matrix

Another risk assessment technique is Consequence/likelihood matrix which is a tool for recording and reporting risks. It’s presented as a matrix that highlights risks based on their consequence and likelihood and combines these characteristics to display a rating for the significance of risk.

Below is an example matrix of the tool which usually has two main characteristics – possible consequences and the likelihood of each consequence happening.

 

 

Consequence

 

 

 

 5 High High Very high Very high
4 Medium High High Very high
3 Low Medium High Very high
2 Low Medium Medium High
1 Low Low Medium Medium
1 2 3 4
Likelihood

In this example, the matrix lists five potential consequences of the wind turbine blade repair work (the potential consequences can be: delayed maintenance, increased maintenance costs, injuries to technicians, damage to the environment, reputational damage etc.), along with an assessment of the likelihood of each consequence occurring. The likelihood is rated as Low, Medium, High or Very high, depending on the probability of the consequence happening. The maintenance manager would then develop strategies to mitigate or avoid these risks.

 

Weibull Analysis and Mean Time Between Failures 

In order to effectively assess and manage risks you need to simultaneously increase reliability of your system or an asset. For this purpose, let’s look into Weibull analysis and Mean Time Between Failures (MTBF), which can be used together to analyze and model the failure rate of a system or an asset over time.

Weibull analysis uses the Weibull distribution to model the time-to-failure data and estimate the reliability of a system. It allows to estimate the failure rate over time and also estimate the probability of failure, as well as the appropriate replacement time and maintenance schedules.

On the other hand, MTBF is a measure of the average time between failures of a system or an asset. It is calculated by summing the total operating time of a system or an asset and dividing it by the number of failures that occurred during that time. 

The formula to calculate the MTBF is: MTBF = total operating time / number of failures

For example, if a system has been in operation for 1,000 hours and had 20 failures, the MTBF would be: MTBF = 1,000 hours / 20 failures = 50 hours.

MTBF is a simple and straightforward measure of system reliability. 

Both Weibull analysis and MTBF can be used to analyze and model the failure rate of a system or an asset over time, however, Weibull analysis provides more detailed information about the failure rate of a system over time and the probability of failure at a given time. Therefore, using both Weibull analysis and MTBF can provide a more comprehensive understanding of the reliability of a system or an asset.

How we help you improve your system reliability and asset integrity

All the above-mentioned risk assessment techniques, analysis and system reliability increase methods are incorporated in and form the basis for our Risk Management Solution, which is an integral part of our enterprise-grade software called Asset Integrity Hub or simply AIH. Besides incorporating international standards and common techniques, our Solution is flexible enough to address the needs specific to your organization.

By using our Risk Management Solution, you can:

  • configure organization specific risk analysis techniques
  • create custom classification of risks and failure modes
  • identify, evaluate, and prioritize potential risks
  • identify possible system failures in advance and define their causes 
  • develop maintenance strategy tailored to your needs

As a result, you gain comprehensive understanding of the reliability of your system, process or assets and have all the information on hand to make decisions about particular risks and how to manage them.

Request a demo now to see in person how we can help you improve your system, process or assets reliability

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