A Rolling Bearing Fault Diagnosis Method Based on Switchable Normalization and a Deep Convolutional Neural Network

Aiming to address the problems of a low fault detection rate and poor diagnosis performance under different loads and noise environments, a rolling bearing fault diagnosis method based on switchable normalization and a deep convolutional neural network (SNDCNN) is proposed. The method effectively extracted the fault features from the raw vibration signal and suppressed high-frequency noise by increasing the convolution kernel width of the first layer and stacking multiple layers’ convolution kernels. To avoid losing the intensity information of the features, the K-max pooling operation was adopted at the pooling layer. To solve the overfitting problem and improve the generalization ability, a switchable normalization approach was used after each convolutional layer. The proposed SNDCNN was evaluated with two sets of rolling bearing datasets and obtained a higher fault detection rate than SVM and BP, reaching a fault detection rate of over 90% under different loads and demonstrating a better anti-noise performance.

[1]  Haidong Shao,et al.  Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain , 2022, IEEE/ASME Transactions on Mechatronics.

[2]  G. Litak,et al.  The influence of the radial internal clearance on the dynamic response of self-aligning ball bearings , 2022, Mechanical Systems and Signal Processing.

[3]  Yueying Wang,et al.  Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine , 2021 .

[4]  Lixiao Cao,et al.  Multi-source feature extraction of rolling bearing compression measurement signal based on independent component analysis , 2021 .

[5]  Zhiming Wang,et al.  Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network , 2020, Shock and Vibration.

[6]  Tielin Shi,et al.  A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures , 2020, Sensors.

[7]  Xin Zhang,et al.  Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosis , 2020 .

[8]  Baoping Tang,et al.  Pattern recognition of a sensitive feature set based on the orthogonal neighborhood preserving embedding and adaboost_SVM algorithm for rolling bearing early fault diagnosis , 2020, Measurement Science and Technology.

[9]  He Cheng,et al.  A novel method of composite multiscale weighted permutation entropy and machine learning for fault complex system fault diagnosis , 2020 .

[10]  Jiafu Wan,et al.  Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images , 2020, IEEE Transactions on Industrial Informatics.

[11]  Jong-Myon Kim,et al.  Fault Diagnosis of Rotary Machine Bearings Under Inconsistent Working Conditions , 2020, IEEE Transactions on Instrumentation and Measurement.

[12]  Ying Zhang,et al.  An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image , 2020, Measurement.

[13]  Houguang Liu,et al.  Optimal IMF selection and unknown fault feature extraction for rolling bearings with different defect modes , 2020, Measurement.

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

[15]  Dongyang Dou,et al.  Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine , 2020 .

[16]  Hui Wang,et al.  A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN , 2020, IEEE Transactions on Instrumentation and Measurement.

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

[18]  Ke Li,et al.  An Adaptive Spectral Kurtosis Method and its Application to Fault Detection of Rolling Element Bearings , 2020, IEEE Transactions on Instrumentation and Measurement.

[19]  Xiangdong Wang,et al.  Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis , 2020, Measurement.

[20]  Xiangdong Wang,et al.  Rolling bearing fault diagnosis based on improved adaptive parameterless empirical wavelet transform and sparse denoising , 2020 .

[21]  Wen Yang,et al.  A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings , 2020 .

[22]  Alexander Hauptmann,et al.  Simultaneous Bearing Fault Recognition and Remaining Useful Life Prediction Using Joint-Loss Convolutional Neural Network , 2020, IEEE Transactions on Industrial Informatics.

[23]  Weiguo Huang,et al.  Time-Frequency Squeezing and Generalized Demodulation Combined for Variable Speed Bearing Fault Diagnosis , 2019, IEEE Transactions on Instrumentation and Measurement.

[24]  Ruimao Zhang,et al.  Switchable Normalization for Learning-to-Normalize Deep Representation , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Jinfeng Zhang,et al.  Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[26]  Andrew Ball,et al.  Fault feature extraction for rolling element bearing diagnosis based on a multi-stage noise reduction method , 2019, Measurement.

[27]  Xining Zhang,et al.  Fault diagnosis of rolling bearing under fluctuating speed and variable load based on TCO Spectrum and Stacking Auto-encoder , 2019, Measurement.

[28]  Bin Yao,et al.  Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification , 2019, Comput. Ind..

[29]  Chao Liu,et al.  An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems , 2019, Mechanical Systems and Signal Processing.

[30]  Tian Ran Lin,et al.  An adaptive sensitive frequency band selection method for empirical wavelet transform and its application in bearing fault diagnosis , 2019, Measurement.

[31]  Sanjay Kumar Singh,et al.  Application of Spectral Kurtosis and Improved Extreme Learning Machine for Bearing Fault Classification , 2019, IEEE Transactions on Instrumentation and Measurement.

[32]  Jing Li,et al.  An enhancement denoising autoencoder for rolling bearing fault diagnosis , 2018, Measurement.

[33]  Wei Gao,et al.  An Intelligent Fault Diagnosis Method for Bearings with Variable Rotating Speed Based on Pythagorean Spatial Pyramid Pooling CNN , 2018, Sensors.

[34]  Zhencai Zhu,et al.  Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network , 2018, Applied Sciences.

[35]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[36]  Fan Jiang,et al.  An Improved VMD With Empirical Mode Decomposition and Its Application in Incipient Fault Detection of Rolling Bearing , 2018, IEEE Access.

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

[38]  Wei Jiang,et al.  Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. , 2018, ISA transactions.

[39]  Wei Gao,et al.  A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network , 2018, Sensors.

[40]  Shibin Wang,et al.  Basic research on machinery fault diagnostics: Past, present, and future trends , 2018 .

[41]  Binqiang Chen,et al.  An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network , 2017, Materials.

[42]  Xuefeng Chen,et al.  Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine , 2017, IEEE Transactions on Industrial Informatics.

[43]  Zhengjia He,et al.  Wheel-bearing fault diagnosis of trains using empirical wavelet transform , 2016 .

[44]  Shaopu Yang,et al.  Fault diagnosis of roller bearing based on relative wavelet energy: Fault diagnosis of roller bearing based on relative wavelet energy , 2011 .

[45]  Xuda Qin,et al.  Fault identification and classification of rolling element bearing based on time-varying autoregressive spectrum , 2008 .

[46]  Xinpan Yuan,et al.  An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm , 2021, IEEE Access.

[47]  Liye Cheng,et al.  An Intelligent Fault Diagnosis Method of Rolling Bearing Under Variable Working Loads Using 1-D Stacked Dilated Convolutional Neural Network , 2020, IEEE Access.

[48]  Tianyi Zhu,et al.  An Adaptive Anti-Noise Neural Network for Bearing Fault Diagnosis Under Noise and Varying Load Conditions , 2020, IEEE Access.

[49]  Meiying Qiao,et al.  Deep Convolutional and LSTM Recurrent Neural Networks for Rolling Bearing Fault Diagnosis Under Strong Noises and Variable Loads , 2020, IEEE Access.

[50]  Dechen Yao,et al.  Railway Rolling Bearing Fault Diagnosis Based on Muti-scale IMF Permutation Entropy and SA-SVM Classifier , 2018 .

[51]  Zhengjia He,et al.  A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM , 2012 .