Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of these article series:
The investigation was solid. The logic tree held up. The team identified a real cause — not just a symptom — and documented it cleanly.
Then the corrective action was: “Remind operators to follow procedure.”
This is the corrective action quality gap. It’s the moment where rigorous analysis collapses into a weak response, and the organization quietly accepts that the failure will return. Nobody flags it. The RCA gets closed. The risk stays in the system.
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If maintenance work planning is a production line, then the planner is neither a firefighter nor a dispatcher.
The perfect maintenance planner is a design engineer for executable work.
They enable perfect execution. They hand over readiness, not problems.
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Nobody tells you this when you graduate.
You spend four, five years studying thermodynamics, machine design, strength of materials. You stay late in the lab. You pass your exams. You step into your first role and you work hard, really hard, because that is what you were trained to do. Be competent. Master the technical side. Deliver.
And for a while, that feels like enough.
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When you get your maintenance costs vs. replacement asset value rav down to 2% you join the list of the world best maintenance practitioners. bring your maintenance costs down to under 1% rav and you will be the world best maintenance practitioner
Here is a sure path with the best methods to get your company Replacement Asset Value ratio down to 3%, then 2%, and finally 1% of maintenance costs. Drive maintenance costs down as a percentage of RAV and you’ll eventually reach the heights of operational and maintenance success. The path to take is called Plant Wellness Way EAM System-of-Reliability.
You surely get there with the right mix of physical asset management, maintenance management, and reliability improvement processes combined in an outstanding Accuracy Controlled Enterprise work quality management system.
The Plant Wellness Way EAM methodology gives you a simple solution to installing world class practices and the best business processes into your company to get Operational Excellence results.
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Understanding failure fingerprints in predictive maintenance to prevent bearing failures. Equipment Failure Forensics shows that bearing failure may be prevented if we identify early warning signs during early stages of failure progression.
Bearings are critical components in many machines, and their failure can lead to costly downtime and repairs. Predictive maintenance aims to detect early signs of failure, allowing timely intervention before a breakdown occurs. One powerful concept in this field is failure fingerprints, distinctive patterns or signals that reveal the stage of a bearing’s degradation. Recognizing these fingerprints at different stages helps maintenance teams act precisely and avoid unexpected failures.
This post explores failure fingerprints across six stages of failure progression, from stage 0 (healthy) to stage 5 (severe failure). We will explain what each stage looks like, provide examples of failure fingerprints, and show how understanding these patterns can prevent bearing failures effectively.
The RCA was thorough. The corrective action was implemented. The team moved on — and by every local measure, the investigation was a success.
Three months later, a nearly identical failure occurs at a facility two states away. A different team investigates it, reaches the same conclusions, and implements the same fix.
Nobody connects the dots.
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Special problems require specialist tools.
In RCM, we assign fault-finding tasks to hidden failures. But what happens when the hidden problem is embedded in the maintenance strategy itself?
Reliability models are not universally interchangeable. Getting the right answer depends on choosing the correct mathematical framework for the problem, and using the wrong approach leads to false confidence, wasted budgets, and costly operational errors.
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There is a version of the AI conversation happening in industrial circles that I find deeply unhelpful. It goes like this: AI will either save everything or destroy everything. Plants will either become fully autonomous or engineers will become redundant. The future is either utopian or catastrophic.
Neither framing serves the professionals who actually have to make decisions about AI adoption in real operations, under real constraints, with real consequences for getting it wrong.
The more useful conversation is a quieter one. It asks: what specific problems does AI solve well in industrial settings, what does it not solve, and what does an organization need to have in place before AI can actually deliver value? That is the conversation this post is about.
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This short video describes characteristics of high-performing industrial operations in comparison to low performers, and how to get there.
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In asset-intensive industries, the reliability of equipment and systems is a critical factor in ensuring operational efficiency, safety, and cost-effectiveness. While maintenance and repairs are essential for sustaining asset performance, the most cost-effective approach to reliability starts at the design phase. This concept, known as Design for Reliability (DfR), ensures that assets are engineered to be robust, durable, and capable of meeting performance expectations throughout their lifecycle.
By integrating reliability principles into the initial design and engineering stages, organizations can reduce unplanned downtime, lower maintenance costs, and enhance overall asset performance.
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In Unit 11, we explore the path forward with Reliability Centered Maintenance (RCM) and how it can be used to meet the specific needs of your organization. Whether your environment is currently reactive or you’re looking to optimize an already effective maintenance program, RCM offers powerful tools to reduce downtime, increase availability, and improve overall maintenance strategies. Beyond maintenance, RCM fosters collaboration amongst team members, enhances the quality of technical manuals, and creates a defensible audit trail. It also improves knowledge transmission, ensuring even seasoned experts gain new insights. By using a facilitated working group approach, RCM builds ownership of the process and the results, leading to a higher confidence that “we’re doing it right.” RCM is a structured process that can drive positive change across your entire organization.
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One of the biggest challenges in Continuous Improvement isn’t solving the problem. Understanding and applying CI methodologies effectively is often where teams struggle most.
Every day, manufacturing teams play detective using tools from the CI toolbox. A machine suddenly slows down – RCA. A defect appears that wasn’t there yesterday – Defect handling. Downtime increases – pass down escalation. Customer complaints begin arriving – LSS.
It’s choosing the right CI methodology to solve it.
Too often organizations jump directly into:
The investigation starts before it officially begins.
A senior tech walks past the failed pump, glances at the shaft, and says “Oh, I know what this is — happens every time the humidity drops.” The room nods. Someone opens the RCA template and starts filling in the cause chain. The logic tree gets built around the conclusion, not toward it.
This is tribal knowledge in action. It isn’t malicious and it isn’t lazy. It’s what happens when unwritten, undocumented experience fills the space that objective evidence should occupy. The veteran’s pattern recognition is real and often valuable. The problem is that it goes untested, and the investigation quietly becomes a formality.
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It was a Tuesday morning, the kind that starts quietly and then doesn’t. I was on shift at the cement plant when the alarm hit the control room. One moment everything was running within parameters. The next, the Ball Mill had tripped, and the entire production line was holding its breath.
No dramatic explosion. No single obvious failure. Just silence. The kind of silence in a plant that costs real money by the minute.
I’ve been in reliability engineering long enough to know that the first question is never “what broke?” The right question is always “why did it break here, why now, and why didn’t we see it coming?”
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Many industries are using this formula to manage Multi-Million-dollar assets:
MTBF = Total Operating time ÷ Number of failures
Simple. Easy. Dangerously incomplete.
Simple arithmetic MTBF assumes failures are Random and Constant. It treats asset health as Static.
But real-life industrial assets are not static. Failure rates are not constant.
Assets age. Lubricants degrade. Corrosion accelerates. Fatigue accumulates. Failure rates begin changing long before catastrophic breakdown occurs.
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