Novelty Detection of a Rolling Bearing using Long Short-Term Memory Autoencoder

Abstract

The major challenge in predictive maintenance (PdM) is an insufficiency of failure data used to train the model and the high complexity of industrial plants, where operational conditions changed over time hence the predetermined threshold will change frequently. Novelty detection offers a solution to this problem by detecting anomalies through learning only the normal data. This study implements a novelty detection method as a one-class classification to detect early signs of failure in a rolling bearing using long short-term memory (LSTM) autoencoder. LSTM autoencoder is a deep learning algorithm combining an autoencoder and LSTM network to reconstruct the normal data to learn nonlinear relationships and temporal nature. The model was tested on historical multivariate time-series fault data provided by Case Western Reserve University. The dropout layer is implemented to reduce overfitting by creating an ensemble model of a neural network. The results suggested that the LSTM autoencoder can effectively differentiate between normal and fault patterns of the bearing with up to 92 % accuracy.