国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Motor bearings are commonly subjected to load, transmission, and impact during operation, resulting in bearing failure that ultimately leads to mechanical breakdown of the entire motor. Vibration signal analysis is a widely adopted technique for monitoring and diagnosing motor bearing faults. However, due to the low energy level of the bearing fault signal, it is susceptible to being overwhelmed by noise and other disturbances present in the vibration signal. In order to address this issue, an enhanced empirical mode decomposition method and a novel analytical energy operator are proposed in this study for motor bearing fault diagnosis, based on fault feature extraction and theoretical calculation using the empirical mode decomposition method. In the proposed diagnostic method, an improved empirical mode decomposition technique is employed to decompose the vibration signal into multiple components in order to eliminate background noise and vibration interference. Subsequently, an measurement index called envelope spectrum kurtosis is utilized to quantify the complexity of the vibration signal, enabling selection of appropriate signal components for further analysis. Finally, fault characteristics of motor bearings are extracted from the decomposed signal components using the analytic energy operator. Simulation signals as well as actual motor bearing vibration signals are employed to validate the efficacy and superiority of this proposed methodology.