A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder

Abstract

In modern industrial systems, establishing the optimal maintenance policy for rotating machinery is essential to improve productivity and prevent catastrophic accidents. To this end, many machinery engineers have been interested in condition-based maintenance strategies, which execute the maintenance activity only when the fault symptoms are detected. For more accurate fault detection of rotating machinery, vibration signals have been widely used. However, the vibration signals collected from most real rotating machinery are noisy and nonstationary, and signals from fault states also rarely exist. To address these issues, we newly propose a fault detection framework with a spectrogram and convolutional autoencoder. Firstly, the raw vibration signals are transformed into spectrograms to represent both time- and frequency-related information. Then, a two-dimensional convolutional autoencoder is trained using only normal signals. The encoder part of the convolutional autoencoder is used as a feature extractor of the vibration signals in that it summarizes information on the input spectrogram into the smaller latent feature vector. Finally, we construct the fault detection model by applying the one-class classification algorithm to the latent feature vectors of training signals. We conducted an experimental study using vibration signals collected from a rolling element bearing experimental platform. The results confirm the superiority of the proposed fault detection framework on rotating machinery. In the experimental study, the proposed fault detection framework yielded AUROC values of almost one, and this implies that the proposed framework can be sufficiently applied to real-world fault signal detection problems.