A Neural Network Autoassociator for Induction Motor Failure Prediction

Abstract

We present results on the use of neural network based autoassociators which act as novelty or anomaly detectors to detect imminent motor failures. The autoassociator is trained to reconstruct spectra obtained from the healthy motor. In laboratory tests, we have demonstrated that the trained autoassociator has a small reconstruction error on measurements recorded from healthy motors but a larger error on those recorded from a motor with a fault. We have designed and built a motor monitoringsystem using an autoassociator for anomaly detection and are in the process of testing the system at three industrial and commercial sites. 1 Introduction An unexpected breakdown of an electric induction motor can cause financial loss significantly in excess of the cost of the motor. For example, the breakdown of a motor in a production line during a production run can cause the loss of work in progress as well as loss of production time. When a motor does fail, it is not uncommon to replace it with ...