The TSM-net: a new strategy for insulated bearings intelligent faults diagnosis

Abstract

With the development of power semiconductor devices, pulse width modulation technology is widely used in high-power frequency conversion control motors, which significantly improves the dynamic performance of variable-speed drive system equipment. However, the high-frequency shaft voltage generated during the drive process acts on the bearing to generate high-frequency current. The damage caused by the shaft current sharply shortens the fatigue wear process of the bearing, which in turn leads to premature failure of the bearing. A high insulating ceramic coating is prepared on the outer surface and side face of the inner and outer rings of the bearing by plasma spraying. That is, an insulating protective film is formed on the outer surface of the bearing, which can effectively isolate or reduce the bearing current, prevent the occurrence of electric erosion, and prolong the service life of the variable speed drive system equipment. However, the vibration excitation generated by the variable-speed drive system equipment will cause cracks or fatigue damage to the insulating bearing, resulting in a very complex fault mechanism of the vibration signal. The fault signal characterization lacks a professional signal analysis method, especially the high-reliability, high-precision and long-life high-performance insulating bearing. There is no qualitative formula or characteristic index to explain its failure. To fill this research gap, a new strategy for optimizing the temporal information fusion model and introducing the self-attention mechanism is innovatively developed, and it is named TSM-Net model, and the first attempt is made to realize intelligent identification of insulated bearing faults. Specifically, a multi-channel insulated bearing time information fusion diagnostic model is designed, and the coarse-grained characteristics with timing law are extracted from the measured insulated bearing fault data. Then, the self-attention mechanism is introduced into the designed insulated bearing time information fusion diagnostic model to optimize, and the weight coefficient is continuously updated to calculate the correlation weight between the insulated bearing fault data and the data, so that the final decision of the TSM-Net model is more focused, so as to improve the diagnostic accuracy. Finally, comparing the proposed TSM-Net model with the current five advanced methods, it is found that the proposed TSM-Net model has good diagnostic accuracy for rail transit motor insulated bearing faults, which verifies the effectiveness and superiority of the strategy, and provides a new way for the fault diagnosis of insulated bearings of high-power inverter control motors.

The BT-SAM-Net: a new framework of end-to-end periodic time-series fault diagnosis for aero-pipelines systems

“…Zhang et al [11] proposed variable-weights neural network to solve multi-variable, strongly nonlinear, dynamic and time-varying problem. Yang et al [12] proposed a new strategy for optimizing the temporal information fusion model and introducing the self-attention mechanism is innovatively developed. The gated recurrent unit neural network is combined with additional networks in the above approach to achieve successful applications in various domains.…”