Miguel Pengel — Active Contributor

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?

Asset management in the mining industry is an intricate dance of financial and operational factors. It demands a firm understanding of complex financial concepts to make strategic decisions effectively, one of which is the discount rate. This number, far from being just an abstract figure, can fundamentally shape a business’s strategic path, particularly concerning asset replacement.

The discount rate is essentially the interest rate used to calculate the present value of future cash flows. The concept comes into play when considering an asset replacement. This decision involves weighing costs and benefits over a time horizon that often spans several years. The discount rate serves as the conduit that translates these future values into present terms, enabling apples-to-apples comparisons. Most of use use this in NPV calculations where we evaluate business improvement initiatives, and compare the viability and return of various projects.

Jack-Knife Diagrams, also known as Log-Scatter Plots, serve as an invaluable visual tool in the realm of Reliability Engineering for prioritizing areas of downtime that require improvement. While many engineers rely on Pareto Charts, we will explore the shortcomings of this approach and how the Jack-Knife Diagram overcomes them.

Now, let’s delve into the distinction between the two methods.