Bearing Vibration Abnormal Detection Based on Improved Autoencoder Network

Abstract

In recent years, autoencoders and neural network technologies have been widely studied and applied to abnormal data detection problems of industrial data such as bearing vibration, but there are still problems such as large training data, network parameter initialization, low training efficiency, poor detection effect and so on. To solve such problems, this paper presents an anomaly data detection method combining Mahalanobis distance and autoencoder network. There is a certain correlation between bearing vibration data characteristics, so the Mahalanobis distance of the data is used to quickly detect some abnormal data, which reduces the amount of training data for the self-encoding network. In this research, the autoencoder and the classifier are combined to construct the autoencoder network, which solves the problem of network parameter initialization and significantly improves the training efficiency. The Mahalanobis distance of the data is added to the data features, which improves the anomaly detection effect of the autoencoder network. The sparseness restriction is added to the autoencoder, and a structure that first enhances the dimensionality and then encodes samples is constructed. The structure enhances the feature learning ability and convergence of the autoencoder. Experimental results show that the method presented has better detection results than other abnormal detection methods for low-dimensional bearing vibration data, and it has certain stability and generalization ability.

Keywords

• |autoencoder|bearing vibration|abnormal detection|mahalanobis distance|autoencoder network