A Refined Multiscale Symbolic Diverse Entropy Technique for Detecting Weak Faults in Rotating Machinery

In rotating machinery components, it is difficult to extract incipient weak fault features directly from vibration signals. A fault feature extraction method based on refined multiscale symbolic dispersion entropy (RMSDE) is proposed. This method mainly combines symbolic coding and dynamic complexity evaluation to achieve multi-dimensional feature extraction. By constructing multiple sets of simulation signals to evaluate the correlation performance of the proposed method, the superior performance in complexity estimation, anti-noise interference and computational efficiency is mainly demonstrated. When RMSDE is combined with a self-organizing fuzzy logic classifier (SOF), the results show that the proposed RMSDE-SOF method can achieve high-precision fault diagnosis of rotating machinery. The effectiveness and robustness of the proposed method in fault feature extraction and diagnosis are verified by using the rolling bearing dataset and gear dataset, which contain multiple fault types, and the comprehensive performance of the proposed method is better than that of similar feature extraction methods. The final results show that the proposed method is superior to the other methods in rotating machinery fault diagnosis.