Using predictive maintenance software has become increasingly popular in many industries as a way of analyzing the working life of hardware. This article gives some background to how this software is used and some modern advancements.
Predictive maintenance software started out in the car industry for helping the scheduling of work orders relating to when parts were due to be replaced. Industry leaders saw that most parts could be set an expected lifespan for how long they would be useful based on environment, treatment, etc.
The software takes any historical data available (from testing, actual failure times, etc.) for gauging when parts are going to fail next within a statistical level of error. The key element was to then automatically notify engineers of when the parts should be swapped out, prior to them failing. This notification feeds into work order scheduling and requisitioning spare parts.
The real benefit of this software was the fact that it helped limit the number of failures and the resulting downtime. In the auto industry, this meant customers had a better, more reliable driving experience, as opposed to reactive maintenance when parts actually failed in the car. For the business, the automatic notification quickly became one of the key inputs into work scheduling so the company can more easily manage work scheduling, work rotas and even the hiring of staff.
The downside, some feel, is the waste of replacing a working part before it fails. This cost needs to be offset against the downtime cost of the machine as a whole (reputation/additional damage/etc.)
Many modern industries, such as electronics/computing, have incorporated predictive maintenance software with automated preventative maintenance. For example, million dollar servers are now capable of switching processing over to backup motherboards (before any computer error occurs) that are pre-installed in the servers. It also means the engineers can replace the worn part at their leisure.
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