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Assets and their reliability based on risk analysis methods

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Paulo Pertile's picture
Chemical Engineer, State Electricity Distribution Company

Chemical Engineer with experience in executing various projects and studies in the domains of maintenance engineering, safety engineering, chemical analysis, chromatography, and environmental...

  • Member since 2023
  • 4 items added with 403 views
  • Sep 13, 2023

Due to their vital function and high cost, power transformers represent an important asset in the electrical grid.
However, as they operate under continuous thermal, electrical, and mechanical stress, power transformers are exposed to several types of failures that can reduce their operational life.
Reliability engineering is focused on the costs of failures caused by downtime, system maintenance, spare parts, equipment repairs, staff, and others. In this scenario, many reliability-based methods for risk analysis are used, such as reliability block diagrams (RDB), hazard analysis, failure mode and effects analysis (FMEA), and fault tree analysis (FTA).

Excellent technical options, such as material improvements, planned inspections, and equipment redundancy decreases the associated risks but, on the other hand, normally increase the cost. In this context, risk can be led to ALARA (As Low As Reasonably Achievable) or ALAPA (As Low As Practically Achievable) levels.
Reliability is related to the ability of equipment to function under specified conditions for a specified time or period of time.

The Weibull distribution is a continuous probability distribution that models a wide range of random variables, mostly like a time to failure or time between events, where the observed failure rate can be in the region of increase, decrease, or constant over time.

The objective of reliability assessments is to provide qualitative and quantitative evidence that the use of a component or equipment won't pass through the range of unacceptable risks.
So, it is extremely important that these analyses are done with properly and attention to detail to be effective.

Matt Chester's picture
Matt Chester on Sep 13, 2023

Do you see that utilities are analyzing failure rates in this type of nuanced way or are they still too brute force with their tracking/predicting of failure? 

Paulo Pertile's picture
Paulo Pertile on Sep 13, 2023

Most companies still don't realize the real utility of this kind of tool, and keep predicting failures with data in raw state. This can bring a wrong perception about the real necessity of the time to stop machine opering and made intervention.

Paulo Pertile's picture
Thank Paulo for the Post!
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