A Novel Unsupervised Learning Method Based on Cross-Normalization for Machinery Fault Diagnosis
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 clas...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9086129/ |