Bearing fault diagnosis based on online symbolic aggregation approximation and streaming deep discriminant analysis

Abstract

Rolling bearings are one of the important components of many industrial equipment, and the timely detection of faults in these bearings significantly contributes to ensuring equipment safety. To achieve real-time diagnosis of bearing faults, this paper proposes an online fault diagnosis framework that utilizes online symbolic aggregation approximation (SAX) and streaming deep discriminant analysis. This method uses the moving window approach to segment the vibration data collected online, and then uses SAX for symbolic representation. the obtained icons are input into stacked denoising convolutional autoencoder (AE) for classification. The model consists of several denoising convolutional AEs and a linear discriminant analysis (LDA) module. To accommodate online data changes, while fixing the main structure of the network, the streaming method is used to update the parameters of LDA. Compared with other traditional approaches for diagnosing bearing faults, the proposed method exhibits distinct advantages. Specifically, it utilizes a stable neural network model, updates classifier parameters in real-time, and demonstrates superior computational efficiency. The application results demonstrate the effectiveness of the proposed method.