Anomaly detection in rotating machinery using autoencoders based on bidirectional LSTM and GRU neural networks

Abstract

A time series anomaly is a form of anomalous subsequence that indicates future faults will occur. The development of novel techniques for detecting this type of anomaly is significant for real-time system monitoring. Several algorithms have been used to classify anomalies successfully. However, the time series anomaly detection algorithm was not studied well. We use a new bidirectional LSTM and GRU neural networks-based hybrid autoencoder to detect if a machine is operating normally in this research. An autoencoder is trained on a set of 12 features taken from healthy operating data gathered promptly after a planned maintenance period using vibration sensors. The features taken from new data are then reconstructed using the trained model. If the model accurately reconstructs the features, the machine is in good working order. If the reconstruction exceeds a certain error threshold, the machine is functioning strangely and needs to be serviced.