A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis

Abstract

Although some traditional autoencoders and their extensions have been widely used in the research of intelligent fault diagnosis of rotating parts, their feature extraction capabilities are limited without label information. In response to this problem, this research proposes a hierarchical sparse discriminant autoencoder (HSDAE) method for fault diagnosis of rotating components, which is a new semi-supervised autoencoder structure. By considering the sparsity of autoencoders, a hierarchical sparsity strategy was proposed to improve the stacked sparsity autoencoders, and the particle swarm optimization algorithm was used to obtain the optimal sparsity parameters to improve network performance. In order to enhance the classification of the autoencoder, a class aggregation and class separability strategy was used, which is an additional discriminative distance that was added as a penalty term in the loss function to enhance the feature extraction ability of the network. Finally, the reliability of the proposed method was verified on the bearing data set of Case Western Reserve University and the bearing data set of the laboratory test platform. The results of comparison with other methods show that the HSDAE method can enhance the feature extraction ability of the network and has reliability and stability for different data sets.

Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis

“…For instance, convolutional neural networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs), and auto encoders have been developed for fault diagnosis applications. CNNs are primarily employed for grid-like structured data, including images and speech, whereas auto encoders are predominantly utilized for unsupervised learning tasks such as data dimensionality reduction and feature extraction [8][9][10]. Notably, CNNs have garnered significant research attention.…”

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“…In the study of intelligent diagnosis algorithms, the existing methods show that considering the original data class structure of the data set can not only enhance the feature extraction ability of the model but also improve the reliability and stability of the model [18,19]. Tao et al [20] proposed a novel bearing defect diagnosis model based on semi-supervised kernel local Fisher discriminant analysis, using the density peak clustering technique to generate pseudo-cluster labels to achieve the extraction of optimal classification features.…”