Piezoelectric ceramics cracking is one of the main faults of the ultrasonic motor. According to the morphological mathematics and information entropy, a method based on multi-scale morphological gradient was proposed for ceramics fault feature extraction and degradation state identification. To solve the problem that traditional multi-scale morphology spectral (MMS) entropy cannot exactly describe the performance degradation of the piezoelectric ceramics, multi-scale morphology gradient difference (MMGD) entropy was proposed to improve the sensitivity to the fault. Furthermore, multi-scale morphology gradient singular (MMGS) entropy was presented to reduce the system noise interference to the useful fault information. The disturbance analysis of temperature, load, and noise for MMGD entropy and MMGS entropy was also given in this paper. Combining the advantages of the above two entropies, a standard degradation mode matrix was built to distinguish the degradation state via the grey correlation analysis. The analysis of actual test samples demonstrated that this method is feasible and effective to extract the fault feature and indicate the degradation of piezoelectric cracking in ultrasonic motor.

During the operation of rolling bearings, vibration signals contain abundant state information, which exhibits strong nonstationarity and nonlinearity. It is always arduous to detect the initial damage point during the lifetime. Non-local means (NLM) algorithm can suppress noise and highlight the components of the fault impact, but the problem lies in the determination of parameters which directly affect the result. In this paper, we proposed a signal processing method combined NLM optimized by Fruit fly Optimization Algorithm (FOA) and Teager Kaiser energy operator (TKEO) to detect the initial stage degradation of bearings. First of all, the proposed optimal NLM algorithm is used to denoise the bearing vibration signals which are gathered in the initial stage of bearing degradation. Then, the TKEO algorithm is applied to suppress the non-impulsive components and the periodic impulsive characteristics of the denoised signals are enhanced simultaneously. Furthermore, the analysis of the frequency components in the Teager energy spectrum is conducted to detect whether the bearings are abnormal or not. Experimental and comparative analyses are presented to validate the proposed method in the end.