Anomaly Detection in Predictive Maintenance with Time Series Analysis

Most of the data science use cases are relatively well established by now: a goal is defined, a target class is selected, a model is trained to recognize/predict the target, and the same model is applied to new never-seen-before productive data. An anomaly is an event that is not part of the system's past; an event that cannot be found in the system's historical data. In the case of network data, an anomaly can be an intrusion, in medicine a sudden pathological status, in sales or credit card businesses a fraudulent payment, and, finally, in machinery a mechanical piece breakdown. In the manufacturing industry, the goal is to keep a mechanical pieceworking as long as possible–mechanical pieces are expensive – and at the same time to predict its breaking point before it actually occurs–a machine breakoften triggers a chain reaction of expensive damages. Therefore, a high value is usually associated with the early discovery, warning, prediction, and/or prevention of anomalies.Specifically, the prediction of "unknown" disruptive events in the field of mechanical maintenance takes the name of "anomaly detection".