Decent Reliability

This is the story of Reliability Centered Maintenance, the systematic approach that reduced maintenance workloads by up to 70% across aviation, military, and industrial applications.

In 1960, airlines were spending fortunes on scheduled maintenance yet still experiencing unexpected failures. This paradox led to Reliability-Centered Maintenance—a 7-question framework that eliminates maintenance guesswork by focusing on actual consequences rather than arbitrary schedules. Whether you're maintaining a car, home systems, or industrial equipment, you'll learn to stop over-maintaining expensive parts while preventing costly surprises like backup generators failing when you need them most. The methodology helps you spot small problems before they cascade into big disasters, using the same systematic thinking that revolutionized aviation safety.

The Royal Australian Navy cut preventative maintenance labor by 75%, while power plants save hundreds of thousands per day during peak periods. In 13 minutes, discover how to turn maintenance from a cost center into a competitive advantage by letting actual risk—not routine—drive decisions.

What's actually inside a model library? What's the difference between a model library of physics based and data driven models?

Listen and learn.

What can medicine teach us about keeping machines healthy? Using healthcare as an analogy (with credit to Peter Attia's book Outlive), this episode works through strategy and tactics, why you should only measure something if the result could change what you do, and the difference between condition monitoring and predictive maintenance.

Condition monitoring asks, “Are you healthy right now?” Like checking vital signs, it catches damage early enough to react. Predictive maintenance goes upstream: like cholesterol tests, cancer screening, and continuous glucose monitoring, it looks for the small problems and process upsets before they become big ones.

What do people actually mean by machine learning or AI in asset performance management? This episode explains the three main types of algorithms (unsupervised, supervised, and reinforcement learning) and which of them APM software really uses.

Why is vibration such a useful method for understanding the health of rotating or reciprocating equipment? This episode talks about what vibration means, how it can be used for detection and then examples of how it can be used to diagnose the cause of issues.

This is the opening episode about modeling methods for condition monitoring or predictive maintenance. We talk about theoretical (rule/logic based) versus empirical (data driven) models, give a few examples, and then close with the topic of explainability. There’s a lot more to come on methods, but this is the start!

Increasing production and decreasing maintenance cost; what more could you ask for?

A short discussion on how APM generates value and a few methods for estimating a reasonable expected value.

Why don’t people walk around putting broomsticks on machines anymore?

Reliability Centered Maintenance, Nowlan and Heap, 1978