Predictive maintenance involves using time-based data from trains and planes to determine maintenance needs in advance.
The hardest part is zeroing in on exactly the right moment to repair or replace a part. If done too far in advance, the benefits of longer usage are lost; if done too late, unexpected failures can result. One cutting-edge tool for delivering more precise predictions is machine learning – a form of artificial intelligence whereby computers learn to detect complex patterns by analyzing large data sets. Early applications have shown that machine learning models can deliver significantly more reliable forecasts of maintenance needs than traditional statistical methods.
FINDING THE RIGHT MACHINE LEARNING MODEL