Process Plant Reliability Engineering

Whilst most reliability literature is concerned with “product” reliability engineering, the described methods (e.g., Weibull analysis and FMEA) find relatively little application in a process plant environment. At first glance, the reason for this would seem to be the ratio of (many) Assets to (few) Engineers. However, the real reason is much more interesting. It is because the traditional methods were developed for application in “simple” and “complicated” systems, whereas a process plant is a “complex” system. Our response to this situation has been to trivialize the complex system behavior, for example in the form of a risk matrix. An alternative response would be to apply methods suited for application in complex systems. For example, simulation is absolutely necessary to make optimal decisions in complex systems. The aim of this article series is to present concepts, methods, and tools for tackling the reliability engineering problem in the context of a complex stochastic production system.

by Andrew Kelleher Leave a Comment

A New Paradigm for Reliability Engineering

We live in turbulent times. Transformational times. But what are the possible implications for the production industries and for process plant reliability engineering?

In 2020, Deloitte (Deloitte Touche Tohmatsu Limited) attempted to address the first part of this question by examining four (4) possible scenarios for the future of Europe’s chemical process industry. The results of their study (refer Figure 1) are relevant for many industries and suggest that “proactive transformation” will be required for a profitable and sustainable future (i.e., Scenario 1).

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by Andrew Kelleher 2 Comments

Bridge Theory and Practice with a Phenomenological Asset Model (PAM)

The established tools for process plant reliability engineering are:

Asset Criticality Analysis (ACA)
Reliability-Centered Maintenance (RCM)
Failure Modes and Effects Analysis (FMEA)
Risk-Based Inspection (RBI)

QUESTION: Are you truly impressed by the measurable and significant performance increase that these tools have delivered?

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by Andrew Kelleher 2 Comments

Why “Big Data” is NOT the Holy Grail for Process Plant Reliability Engineering

Data is good. Quality data is better. “Big data” is even fashionable. But it won’t help you solve a “small data” problem.

Let me guess. The quality of your CMMS data is not great, but if it was, you could really do something with it. Since decades, in fact. Just need to re-tweak those failure codes!

And recently, you were very tempted by a consultant’s new “data-driven” approach that promised to deliver staggering results. And why not?

Because it’s not a “big data” problem.

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by Andrew Kelleher 1 Comment

How to Estimate the Stochastic Performance of your Production System in 5 Minutes

“Technical” reliability engineering is “quantitative”. This implies that the benefits of improvement measures (e.g.: design changes or alternative maintenance strategies) can be quantified in terms of system performance parameters. Refer, for example, Figure 1.

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by Andrew Kelleher 5 Comments

Four Reasons to Rethink your Reliability Improvement Journey

The term “reliability improvement journey” is well-established in the chemical process industry. The decade-long, tortuous journey of one company is shown in terms of operational availability (i.e., production) and relative maintenance cost at Figure 1.

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