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A Method of Mechanical Fault Feature Extraction for High-Voltage Circuit Breaker Via CEEMDAN and Weighted Time-Frequency Entropy

The critical step in the mechanical fault diagnoses of a high-voltage circuit breaker (HVCB) is the extraction of mechanical state characteristics. The effectiveness of these characteristics is related to the accuracy of the corresponding diagnostic results. A novel method for the mechanical fault feature extraction of a HVCB via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and weighted time-frequency entropy is proposed in this study. First, the time-frequency decomposition of vibration signals is performed by the CEEMDAN and the Hilbert transform to obtain the instantaneous frequency of intrinsic mode function (IMF) components. Then, the IMFs are reconstructed via a bandpass filter to gain a signal component of the specified frequency band. In addition, the weighted time-frequency entropy feature is extracted from the energy matrix. Through the verification of multiple groups of classifiers, the diagnostic results have a higher accuracy, which indicates that the proposed method can effectively characterize different mechanical faults.