RTSMFFDE-HKRR: A fault diagnosis method for train bearing in noise environment

Abstract

The bearings have been exposed to a noisy environment for an extended period, making it challenging to identify fault characteristics accurately and resulting in low accuracy. In this study, we proposed a technique for diagnosing train-bearing faults in noisy environments using refined time-shift multiscale fractional order fuzzy dispersion entropy and hybrid kernel ridge regression (RTSMFFDE-HKRR) to enhance the precision of fault detection. Firstly, RTSMFFDE is proposed based on multiscale fuzzy dispersion entropy by combining the theories of refinement, time-shifting, and fractional order. Revising the refinement process helps to stabilize the entropy value when dealing with a large scale factor. Implementing time-shifting techniques can help preserve the original signal features more effectively. The theory of fractional order strengthens the noise-resistant performance of the algorithm, and the proposed RTSMFFDE has a superior feature extraction capability. Subsequently, a hybrid kernel function is formulated through the combination of the radial basis kernel function (RBF) and the linear kernel function, aiming to improve the nonlinear mapping ability and anti-noise ability of ridge regression classification algorithm. Finally, two sets of experimental cases were used to verify the proposed RTSMFFDE-HKRR. The results show that the fault feature information extracted by RTSMFFDE in noisy environment is more comprehensive, and the classification effect of HKRR is more significant. In the fault diagnosis of two bearing simulation test beds, the accuracy rate of RTSMFFDE-HKRR is up to 100%. In the noisy environment, the accuracy rate is 98.84% and 97.32%, which is much higher than other diagnostic models. RTSMFFDE-HKRR is suitable for train bearing fault diagnosis in noisy environment.