Miguel Pengel — Active Contributor

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…]

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.

Every Reliability Engineer will be familiar with the Weibull Analysis. Most of us even have an Excel template laying around that we refer to!

The problem is, that when we have to handle Suspended data (e.g. components that haven’t failed yet at time of observation), the Excel sheet must use VBA in the background if the user wants a “single-button” tool.

The mining industry in Australia has for some time been plagued by high turnover in its skilled maintenance workforce. The tough lifestyle and remote/regional locations are some of the main contributing causes, and have been for years, however other industries of employment offering competitive wages and a better lifestyle have in the recent years pulled the workforce from mining, leaving a void of labor numbers behind that have proved difficult to fill.

The common query, “How do I keep equipment reliability up, even with high turnover?” prompts a standardized response from most maintenance professionals – proceduralize the maintenance system to reduce variability and maintain consistent output. However, while this approach bears truth, it overlooks a substantial aspect…

Industrial operations that have operating horizons exceeding the lifespan of their assets face a crucial decision as they approach this timeline’s end (but not enough to operate the equipment until its full economically optimal life).

Specifically, they must decide whether to overhaul the asset, replace it with a new one, or rent the equipment until operations conclude. Given the numerous variables with inherent uncertainties in the financial models, how can they be confident in their decision?