Fault diagnosis of planetary gear backlash based on motor current and Fisher criterion optimized sparse autoencoder

Abstract

Planetary gear reducer is widely applied in various transmission equipment, and its performance highly affects the operation of a machine. The appearance of unreasonable backlash in planetary gear reducer may lead to undesirable vibration, which may accelerate the degradation of equipment and eventually cause premature failure. In traditional condition-based monitoring (CBM), sensors such as accelerometers have been utilized to detect the fault of planetary gear. However, the complexity and integration of planetary gear limit the installation and application of vibration sensors in practice. In this case, the current signal from motor, as a convenient real-time monitoring approach, is introduced into the CBM of the planetary gear. In this paper, a fault diagnosis method based on drive motor current signal analysis (MCSA) is presented to identify the backlash fault in a planetary gear reducer. In this method, frequency domain data of the original current signal is found and used to automatically extract fault features. A deep sparse autoencoder (DSAE) extracts the required features. In particular, the Fisher criterion is introduced to evaluate the sensitivity of these features. It then selects the most effective ones for improving diagnostic accuracy as well as diagnostic efficiency. Experimental test data shows that under different load conditions, this method outperforms other typical fault diagnosis methods and exhibits the best performance.

Research on an SAE-based Substation Noise Monitoring Method

“…The sparse autoencoder (SAE), proposed by Olshausen, makes up the disadvantage of traditional autoencoders that the effective features cannot be extracted easily [3] . Using multi-layer nonlinear transformation, the SAE extract hidden distribution features from the original data.…”

“…The sparse autoencoder (SAE) can adaptively extract feature information for classification from a large quantity of data through data studying and self-training. The SAE has been applied in image recognition, voice recognition, and other areas, receiving remarkable achievements [3] . This paper proposes an SAE-based transformer state recognition method.…”