A Hybrid Approach for Fault Diagnosis of Railway Rolling Bearings Using STWD-EMD-GA-LSSVM

Abstract EN

Vibration signals resulting from railway rolling bearings are nonstationary by nature; this paper proposes a hybrid approach for the fault diagnosis of railway rolling bearings using segment threshold wavelet denoising (STWD), empirical mode decomposition (EMD), genetic algorithm (GA), and least squares support vector machine (LSSVM).

The original signal is first denoised using STWD as a prefilter, which improves the subsequent decomposition into a number of intrinsic mode functions (IMFs) using EMD.

Secondly, the IMF energy-torques are extracted as feature parameters.

Concurrently, a GA is employed to optimize the LSSVM to improve the classification accuracy.

Finally, the extracted features are used as inputs for classification by the GA-LSSVM.

Actual railway rolling bearing vibration signals are used to experimentally verify the effectiveness of the proposed method.

The results show that the novel method is effective and accurate for fault diagnosis of railway rolling bearings.