Monitoring and Diagnosis of Motor Operation State Fault by LBP-SVM
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Abstract
Motors as a major power source are highly influential to current development and the wide application of intelligent devices. However, considering common use in experimental environments or construction sites, such a more complex working environment is not feasible for basic diagnosis of conventional fault operations. In this paper, the time-frequency thermogram detection method is adopted, the best wavelet base is selected to obtain the wavelet time-frequency two-dimensional image and then Tamura texture feature extraction on the best time-frequency image is conducted. In addition, the time-frequency image features are strengthened by local binary feature extraction and then the diagnostic experiments on the motor faults using the support vector machine. According to the final experimental results, the correct rate of fault detection is more than 93.7% when the value of LBP is 2. The average accuracy of SVM and PNN classification algorithms for motor fault detection both exceed 90%, but SVM averages are higher than PNN averages, which proves the superior performance of the LBP-SVM-based model.
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