Anomaly Detection in a Combined Driving System based on Unsupervised Learning

Anomaly detection models using big data generated from facilities and equipment have been adopted for predictive maintenance in the manufacturing industry. When facility faults or defects occur, different patterns of abnormal data are shown owing to their component behaviors. By detecting these pattern changes, it is possible to determine whether a facility abnormality occurs. This study evaluated the anomaly detection results from a combined driving system consisting of three driving motors for about six months at a manufacturing site. The learning data with an autoencoder model for about a month at the beginning of vibration data collection and continuous monitoring of anomalies using reconstruction errors showed that a component defect occurred in one driving motor, and the reconstruction error increased progressively about three months earlier than a facility manager found the failure. In addition, the micro-electro-mechanical systems sensor showed high amplitude in the entire frequency domain when high reconstruction errors occurred. However, the integrated electronics piezoelectric sensor showed different patterns as high amplitude in a specific frequency domain. The results of this study will be helpful for detecting facility abnormalities in combined driving systems using vibration sensors.