Bi-LSTM fault diagnosis method for rolling bearings based on segmented interception AR spectrum analysis and information fusion

Abstract

The rolling bearing fault diagnosis is affected by industrial environmental noise and other factors, leading to the existence of some redundant components after signal decomposition. At the same time, the existence of the modal aliasing phenomenon in empirical mode decomposition (EMD) and the relevant improved algorithms also leads to the existence of many invalid features in the components. These phenomena have great influence on the bearing fault diagnosis. So a rolling bearing bidirectional-long short term memory (Bi-LSTM) fault diagnosis method was proposed based on segmented interception auto regressive (SIAR) spectrum analysis and information fusion. The ensemble empirical mode decomposition (EEMD), the complementary ensemble empirical mode decomposition (CEEMD) and the robust EMD (REMD) algorithms decompose the rolling bearing fault signals, and AR spectrum analysis is performed on the obtained components respectively. By comparing the AR spectra of the components corresponding to different fault locations, the effective AR spectral values are intercepted as the eigenvalues of the data, and finally all the eigenvalues are fused to achieve the purpose of screening effective features more efficiently so as to reduce the impact of feature redundancy caused by mode aliasing on neural network training. Then the Bi-LSTM neural network was used as a rolling bearing fault diagnosis classifier, and the simulation experiments were conducted based on the rolling bearing fault signal data from Case Western Reserve University to verify the effectiveness of the proposed feature extraction and fault diagnosis method.

Deep Learning Network Based on Improved Sparrow Search Algorithm Optimization for Rolling Bearing Fault Diagnosis

“…An, Yiyao et al [8] introduced a sparse attention mechanism into LSTM for the fault diagnosis of rotating machinery, which has significant advantages in reducing random interference and enhancing feature information. Zhong, Cheng et al [9] proposed a bi-directional longand short-term memory fault diagnosis method for rolling bearings based on segmental interception spectrum analysis and information fusion, which reduces the impact of feature redundancy on the training neural network arising from modal blending by comparing the AR spectra of the components corresponding to different fault locations and fusing all features. Wang, Haitao et al [10] introduced a self-calibrating convolutional module in the residual network and proposed a recurrent neural network based on two-stage attention to achieve good results in bearing fault diagnosis experiments.…”