Feature Extraction Method for Fault Diagnosis of Machine Unit Based on Wavelet Singularity Principle and Immunology Optimization Principle
Consider on fault signal coupling of machine unit for fault feature extraction caused by difficult problems, wavelet singularity theory be used complex fault feature extraction. Fault signal after wavelet denosing, which use clonal selection for fault classification. In the new feature space, the characteristics of different types of fault modes enhanced data aggregation, the different fault can be divided, and the compound fault signal will be separated, thereby enhancing the accuracy of fault diagnosis, fault samples of analog complex machine unit be trained, detected, and diagnosed, results to be verified.
Keywordswavelet singularity principle clonal selection fault diagnosis
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