ANOMALY DETECTION FOR LARGE FLEETS OF INDUSTRIAL EQUIPMENT: UTILIZING MACHINE LEARNING WITH APPLICATIONS TO POWER PLANT MONITORING

This paper covers three contemporary topics in the development and deployment of machine learning based diagnostics for large fleets of industrial machines. First, we address the philosophy of monitoring as to whether anomaly detection versus specific failure classification should be pursued, utilizing published statistics of reliability of industrial machines. Second we address the question of unsupervised versus supervised methods using a simulated example of a typical industrial machine fault, where we apply a number of popular unsupervised and supervised algorithms and directly compare their alerting ability. Lastly, model development and deployment at global scale is discussed, with application to a global fleet of gas turbines. The application includes a framework of neural network models that have been trained to find anomalous behavior for a system of the gas turbine package. The remainder of the paper includes a discussion of the results from the fleet application. Specifically, we discuss the fleet training procedure and hardships incurred in moving from proof of concept designs to full deployment on global production asset monitoring. Selected training models that failed to be of production quality are examined and the source of training error is identified. Throughout, the paper provides lessons learned, broad insights gained, and productionization issues that still need improvement as it relates to development and deployment of machine learning models at the scale of global industrial machine monitoring.