A Novel Unsupervised Learning Method Based on Cross-Normalization for Machinery Fault Diagnosis

Abstract

Sparse representation is the important principle of unsupervised learning method. In order to accurately identify the fault condition of machines, the desired feature distribution should show population sparsity and lifetime sparsity. In this paper, to improve the accuracy and robustness of the classification, a novel fault diagnosis method named Cross-sparse Filtering (Cr-SF) is proposed based on the cross l1/2-norms of the feature matrix, which mean the population sparsity and lifetime sparsity terms. After the weights training process, a novel nonlinear activation function is used for feature extraction in the test process. Cr-SF can learn discriminative features from the raw data and accurately identify the fault condition. Rolling bearing fault and gear-box fault datasets are employed to validate the performance of the proposed method. The verification results confirm that Cr-SF is an effective tool for handling big data. The robustness and accuracy of the classification results using Cr-SF are comparable to convolutional networks with a much faster training process.

Keywords

• Cross-normalization
• unsupervised learning
• intelligent fault diagnosis
• rotating machinery
• big data