Miguel Pengel — Active Contributor

A component-level Weibull reliability analysis of the AirPods Pro 2, and a hard look at whether the $29 extended warranty is protection or a profit centre.

I normally write a lot about asset management within the heavy industrial space, but a recent “domestic” applicance failure made me think about the economics and failure characteristics of everyday products- a little off subject, but defintiely on-topic!

My AirPods Pro 2 failed exactly one month after the standard warranty expired. The left earbud’s battery had degraded to the point where it would die within 45 minutes, rendering a $249 product functionally useless. Rather than just buying a new pair, I did what any reliability engineer would: I modelled the failure.

[Read more…]

If you’ve worked in reliability or maintenance for any length of time, you’ve probably sat through a meeting where someone asks “so how many spares do we need?” and the answer is basically “well, we’ve always kept two on the shelf.” Or the classic PM frequency debate — why is it every 6 months? Because it’s always been every 6 months.

That kind of thing bugged me. Not because people are lazy, but because the right tools to do it properly are usually locked behind expensive software or buried in a textbook somewhere. So I built four free, browser-based calculators that cover the core decisions reliability engineers deal with all the time. No logins, no licence fees, no fluff. [Read more…]

My father is an industrial engineer/ manager (retired). He spent his career in maintenance and operations management, and growing up, I absorbed a principle so fundamental that it shaped how I see the world: if you want something to last, you have to look after it.

It wasn’t complicated philosophy. It was watching him maintain the family car religiously—not because it was broken, but because he understood what neglect would cost later. It was seeing him make sure the wooden fence always had a healthy coating of Carbolineum and the gutters were cleared before the rainy season arrived. The lesson was simple: care for things before they demand it, and they’ll serve you well. Ignore them, and they’ll fail you at the worst possible moment.

This principle is obvious to anyone who’s grown up around machinery. Yet somehow, in the complexity of industrial operations, it gets lost. Maintenance teams fight for resources, justify their existence, and struggle to communicate their value to organisations that measure success in tonnes and throughput. Production teams, under relentless pressure to hit targets, see maintenance as a necessary interruption at best—and an obstacle at worst.

The root of this disconnect isn’t ignorance or bad intention. It’s something more fundamental: the success of maintenance is invisible. [Read more…]

It’s 2 AM. Your SAG mill gearbox just failed. The plant is down, losing $95,000 every hour. You call the warehouse: “Do we have a spare?”

That answer could mean the difference between a 4-hour repair and a 4-week nightmare waiting for an emergency shipment from overseas. The frustrating thing is, someone made a decision months or years ago about whether to stock that gearbox. They either got it right, or they didn’t. Tonight you find out which.

This scenario plays out daily across mining and processing operations worldwide, and most organizations don’t know they’ve got it wrong until that phone call comes. They’re either sitting on millions in dusty inventory that’ll never be used, or they’re one failure away from catastrophe. The sweet spot in the middle is surprisingly hard to find. [Read more…]

Many years back when I started my Asset Management journey, the ISO55000 series of standards was always lingering overhead as the “bible” of asset management. The problem was that the systems and processes we tend to learn in this field stem from those standards, but rarely do we deep dive into the documentation and try to mind map for ourselves how it all fits together. [Read more…]

At Pardus Consulting, we’ve just launched an online Jack-Knife Diagram (JKD) calculator that transforms downtime data into a clear, actionable visual.

By entering failure codes, counts of interventions and MTTR values, you’ll see at a glance which issues are both frequent and severe—no manual plotting or spreadsheet gymnastics required.

We’ve previously talked about this method as a much needed upgrade from Pareto Charts when prioritising downtime events and focus areas in our article HERE. [Read more…]

Optimum replacement time – Theory

The optimal asset replacement time calculation has one goal, and that is to calculate the lifetime of when assets should be operational as to have the lowest possible operating cost over its lifetime.

This means balancing two opposing forces, namely the increasing operational cost and unreliability of the asset as it ages, with the decreasing effective cost of ownership. [Read more…]

In most industries we ideally want the lowest-cost maintenance strategy, one that balances risk of unplanned vs planned maintenance costs to a lifetime minimum.

This is what we’ve calculated in a previous article HERE, and this article/exercise is a follow-on from this.

The downside of using this method is that it may select a maintenance interval where the inherent reliability of the component at changeout time is less than is required for operational reasons, or that we’re not getting the best “increase in reliability vs marginal cost” value. [Read more…]

You don’t realise how important it is to get your critical spares holding right until your $50K/hr machine is sitting dead with an emergency part all the way in a different city.

So how does your warehouse ensure you have what you need, when you need it? Who decides how much to stock?
Well… in most cases on mining sites there is no real math behind this number, and that’s a shame considering how much not having spares costs us in lost opportunity.

When Weibull analysis is applied to complex, repairable systems – like mining equipment – care must be taken to ensure the analysis is applied to failure data exhibiting a common failure mode on the component-level, not the system-level.

This requires the reliability engineer to review the work order data at times and, ideally, the failed components themselves. Otherwise, the “Garbage In, Garbage Out” principle applies.

In a previous article we covered how to perform a detailed Weibull Analysis in Excel. The outputs from a Weibull analysis are important because we can use them for a variety of Reliability calculations such as when to most economically maintain assets.

A common mistake we see made is Reliability Engineers determining the optimal maintenance interval as the MTBF. This is incorrect as it assumes that you will have failed approximately 50-60% of the components before you have maintenance performed on them. (Sounds ridiculous when you say it that way right!?)

It’s no surprise that the majority of Australia Mining companies don’t leverage the full use of their data to manage their (expensive) assets better. Industry seems to think this is just an adoption issue, where the term “If it ain’t broke don’t fix it” comes to mind. But the reality is that there are far more underlying issues we need to consider why Mining is lagging behind other major industries such as Oil and Gas, Aviation and Power Generation, and yes.. what we do with all our data plays a big part.