The two key problems in implementing predictive modeling for Condition Based Maintenance (CBM) are its cost and the accuracy of the results. Traditional machine learning and statistical approaches require highly trained data scientists who are both expensive and in short supply.
Imagine that the engineers responsible for maintenance could conduct the analysis themselves. Imagine that they do not need any historical data but instead they can use any suspicious pattern they detect to track and recover other equipment. Imagine that can be done with over 90% accuracy. More