A Sparse Learning Method with Regularization Parameter as a Self-Adaptation Strategy for Rolling Bearing Fault Diagnosis

Abstract

Sparsity-based fault diagnosis methods have achieved great success. However, fault classification is still challenging because of neglected potential knowledge. This paper proposes a combined sparse representation deep learning (SR-DEEP) method for rolling bearing fault diagnosis. Firstly, the SR-DEEP method utilizes prior domain knowledge to establish a sparsity-based fault model. Then, based on this model, the corresponding regularization parameter regression networks are trained for different running states, whose core is to explore the latent relationship between the regularization parameters and running states. Subsequently, the performance of the fault classification is improved by embedding the trained regularization parameter regression networks into the sparse representation classification method. This strategy improves the adaptability of the sparse regularization parameter, further improving the performance of the fault classification method. Finally, the applicability of the SR-DEEP method for rolling bearing fault diagnosis is validated with the CWRU platform and QPZZ-II platform, demonstrating that SR-DEEP yields superior accuracies of 100% and 99.20% for diagnosing four and five running states, respectively. Comparative studies show that the SR-DEEP method outperforms four sparse representation methods and seven classical deep learning classification methods in terms of the classification performance.

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

“…Fault category Experiment samples Diagnosis accuracy (%) [227] FKP-SGECNN 10 -99.63 [228] Improved graph convolutional network (GCN) 28 1400 99.12 [229] BCMFDE-RF-mRMR-KNN 10 550 99.09 [230] NWMF-CNN 10 4000 99.80 [231] Reinforcement neural architecture search CNN 12 3600 99.65 [232] Dimension expansion and AntisymNet lightweight CNN 10 10 000 99.70 [233] Multi-scale weighted graph-MCGCN 10 3000 99.45 [234] Improved (ICEEMDAN)-ICA-FuEn 10 10 000 99.91 [235] MAM-DSDCNN 7 2800 99.63 [236] Sparse representation deep learning (SR-DEEP) 4 2000 100.00 [237] Online sequential extreme learning machine (OS-ELM) 4 9466 99.62 [238] IFE + CBAM-enhanced InceptionNet 10 1000 99.5 [239] SSCL method based on MSA mechanism and MCL 10 2000 99.97 [240] MRDNN-AG 10 120 000 98.85 [241] AMCEEMD-1DCNN 7 3500 99.50 [242] Modified AlexNet-SVM 4 -99.60 [243] FC-CLDCNN 10 10 000 99.95 [244] PCA-ICEEMDAN and BiLSTM-SCN-CCAM 10 1024 99.92 [245] 2ADA + MK-MMD 10 1960 99.76 [246] 1D feature matching domain adaptation 3 9000 100.00 [247] ICEEMDAN-Hilbert transform-CBAM 10 30 000 95.2 [248] Ensemble MSRCNN-BiLSTM 4 4800 98.43 [249] WKN-BiLSTM-AM 10 1750 99.7 [250] MVO-MOMEDA-SVM 4 400 92.50 [251] WPDPCC-DGCL 10 6000 98.65 [252] I-PixelHop framework based on Spark-GPU 10 -98.93…”

Convolutional neural network intelligent fault diagnosis method for rotating machinery based on discriminant correlation analysis multi-domain feature fusion strategy

“…(1) SR-DEEP [32], (2) BFD-2DCNN [33], (3) LSSA-VMD-GRU [34], (4) WPD-CSSOA-DBN [35], (5) ACPSO-BP [36]. The experimental results are shown in Fig.…”