Multiscale convolutional neural network and decision fusion for rolling bearing fault diagnosis

Purpose The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing. Design/methodology/approach To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results. Findings The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models. Originality/value The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

[1]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[2]  Behrooz Rezaie,et al.  Fuzzy-model-based fault detection for nonlinear networked control systems with periodic access constraints and Bernoulli packet dropouts , 2019, Appl. Soft Comput..

[3]  K. Loparo,et al.  HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings , 2005 .

[4]  A. Rosenkranz,et al.  The Use of Artificial Intelligence in Tribology—A Perspective , 2020, Lubricants.

[5]  Yaguo Lei,et al.  Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .

[6]  Yuanyuan Wu,et al.  Physical and chemical indexes of synthetic base oils based on a wavelet neural network and genetic algorithm , 2019 .

[7]  Hazem Nounou,et al.  Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems , 2020 .

[8]  Zhipeng Li,et al.  Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network , 2019, Renewable Energy.

[9]  Chao Liu,et al.  A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults , 2019, Knowl. Based Syst..

[10]  Xinyu Shao,et al.  Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings , 2020, Appl. Soft Comput..

[11]  Junsheng Cheng,et al.  An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis , 2019, Neurocomputing.

[12]  Yan Han,et al.  An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes , 2019, Comput. Ind..

[13]  Huijun Gao,et al.  A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis , 2019, Neurocomputing.

[14]  Nenzi Wang,et al.  Assessment of artificial neural network for thermohydrodynamic lubrication analysis , 2020 .

[15]  Jianhua Cai,et al.  Gear fault diagnosis based on a new wavelet adaptive threshold de-noising method , 2019, Industrial Lubrication and Tribology.

[16]  Yuan Xu,et al.  Fault diagnosis using novel AdaBoost based discriminant locality preserving projection with resamples , 2020, Eng. Appl. Artif. Intell..

[17]  Wang Zhenya,et al.  Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine , 2020 .

[18]  Sylvain Verron,et al.  A decision fusion based methodology for fault Prognostic and Health Management of complex systems , 2019, Appl. Soft Comput..

[19]  Qiang Fu,et al.  Domain adaptive deep belief network for rolling bearing fault diagnosis , 2020, Comput. Ind. Eng..

[20]  Pengcheng Jiang,et al.  Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network , 2019, Comput. Ind..

[21]  Qiang Fu,et al.  Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network , 2020 .