Research on rolling bearing fault diagnosis under strong noise background and variable speed working condition

Abstract

The working environment of coal mine mechanical equipment is harsh, the background noise is strong, and the early fault characteristic information of the bearing is weak. Therefore, it is difficult to extract the information reflecting the fault state from the vibration signal measured by the sensor. Moreover, the coal mine mechanical equipment work in high speed, shock and other working conditions, which are typical non-stationary working conditions. The unstable excitation and complex working conditions directly lead to the difficulty of extracting the bearing fault characteristic signal. In order to solve the above problems, a rolling bearing fault diagnosis method based on computed order analysis and adaptive stochastic resonance is proposed in the background of the working conditions of mine hoisting equipment. Firstly, the method simulates the typical variable speed working conditions in the operation of mine hoist, constructs the fault simulation signals and collects the experimental signals of bearing vibration. Secondly, by collecting synchronous time-domain key-phase signal at equal angles, the non-stationary vibration signal of the bearing is resampled into a stationary signal by using computed order analysis. Thirdly, the stationary signal is decomposed into a number of intrinsic mode function (IMF) components by using the variational mode decomposition (VMD) method, and the bearing fault type is judged by the bearing fault order. Finally, the adaptive stochastic resonance method is used to enhance the bearing fault characteristic order so as to achieve the extraction and enhancement of fault characteristics for fault diagnosis. The simulation and experimental results prove the effectiveness of the method. And the method is compared with the maximum correlation kurtosis deconvolution (MCKD) method. The results show that although the MCKD method can also observe the fault characteristic order, but the characteristic order is only 0.001 96 higher than the amplitude of the surrounding interference order, which is lower than the results of the proposed method, indicating the superiority of the proposed method.

Keywords

• coal mine mechanical equipment; mine hoisting equipment; strong noise; variable speed; rolling bearing fault diagnosis; computed order analysis; adaptive random resonance