An Improved Hybrid CNN-SVM based Method for Bearing Fault Diagnosis Under Noisy Environment

Deep learning (DL) algorithms for bearing fault diagnosis have attracted much attention in recent years, which can overcome the disadvantages of conventional intelligent methods in feature extraction. Deep learning based approach provides can automatically extract the features from raw data. Convolutional neural network (CNN) is an effective method and support vector machine (SVM) is an efficient classifier. In this paper, a novel improved hybrid CNN-SVM based model is proposed for bearing fault diagnosis through transforming the vibration signal into an image with two-dimensional representation. In addition, through adding the features clustering distance into loss function, the model robustness against environmental noises is significantly improved. The proposed solution is evaluated through experiments and the numerical results demonstrate its effectiveness in identifying various faults in bearings.

[1]  Qiang Yang,et al.  Sparse component analysis-based under-determined blind source separation for bearing fault feature extraction in wind turbine gearbox , 2017 .

[2]  Moncef Gabbouj,et al.  Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.

[3]  Diego Cabrera,et al.  Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals , 2016 .

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[5]  Hee-Jun Kang,et al.  Rolling element bearing fault diagnosis using convolutional neural network and vibration image , 2019, Cognitive Systems Research.

[6]  Ching Y. Suen,et al.  A novel hybrid CNN-SVM classifier for recognizing handwritten digits , 2012, Pattern Recognit..

[7]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[8]  Liang Gao,et al.  A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[9]  Haidong Shao,et al.  Rolling bearing fault diagnosis using an optimization deep belief network , 2015 .

[10]  Iqbal Gondal,et al.  Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach , 2015, IEEE Transactions on Industrial Electronics.

[11]  Wei Zhang,et al.  A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning , 2018, Neurocomputing.

[12]  Chen Lu,et al.  Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification , 2017, Signal Process..

[13]  A. Karahoca,et al.  Neural network based motor bearing fault detection , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[14]  Feng Jia,et al.  An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.

[15]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[16]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.