Predictive Maintenance is the new buzz word in Manufacturing these days, thanks to advances in Machine Learning methods, specifically predictive analytics methodologies. However, I always suggest professionals in any domain, to not just chase topics that are hot. It is important to have a broad depth of fundamentals, in order to appreciate why some approaches are better than the other. So in this post, we will discuss some of the fundamental maintenance approaches. Outlined below are the more widely used maintenance management strategies, as well as their pros and cons and situations when they are best applied
With reactive maintenance, the machine is used to its limit and repairs are performed only after the machine fails. If you’re maintaining an inexpensive system like a light bulb, the reactive approach may make sense. But think of a complex system with some very expensive parts, such as an aircraft engine. You can’t risk running it to failure, as it will be extremely costly to repair highly damaged parts. But, more importantly, it’s a safety issue.
Many organizations try to prevent failure before it occurs by performing regular checks on their equipment. One big challenge with preventive maintenance is determining when to do maintenance. Since you don’t know when failure is likely to occur, you have to be conservative in your planning, especially if you’re operating safety-critical equipment. But by scheduling maintenance very early, you’re wasting machine life that is still usable, and this adds to your costs.
Predictive maintenance lets you estimate time-to-failure of a machine. Knowing the predicted failure time helps you find the optimum time to schedule maintenance for your equipment. Predictive maintenance not only predicts a future failure, but also pinpoints problems in your complex machinery and helps you identify what parts need to be fixed.
As you can infer from the descriptions above, for critcal Manufacturing machinery, Predictive Maintenance is the most optimal method. The good news is that unlike a decade ago, building Predictive Maintenance capability has become pretty inexpensive, assuming you already have the other “Smart” infrastructure in place that is effectively capturing the data prudently.