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Machine-learning based predictive maintenance is critical for any business with high-capital assets, especially the transportation and manufacturing sectors, as it harnesses the power of AI to control rising equipment maintenance costs. Where a machine learning approach adds value is that the system can take data from a variety of sources, combine it, and apply algorithms that anticipate equipment failure before it occurs, often more accurately than a human.
Predictive maintenance often allows the detection of impending failures that could never be detected by human eyes. With predictive maintenance, downtime and repairs are directly tied to likely failure, minimizing cost (e.g. less labor time, less chance of unexpected failure) and maximizing asset life. By contrast, traditional maintenance techniques (run-to-failure, preventative, or some combination of the two) inevitably mean unexpected repair, which leads to longer downtime on top of unnecessary downtime due to regular inspection.
PwC defines Predictive Maintenance 4.0 as continuous real-time monitoring of assets, with alerts sent based on predictive techniques, such as regression analysis. They estimate that only 11 percent of companies have already achieved this level of predictive maintenance.