A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.

[1]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[2]  Gaoliang Peng,et al.  Rolling Element Bearings Fault Intelligent Diagnosis Based on Convolutional Neural Networks Using Raw Sensing Signal , 2017 .

[3]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[4]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[5]  A. K. Wadhwani,et al.  Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis , 2011 .

[6]  Peng Chen,et al.  An Intelligent Diagnosis Method for Rotating Machinery Using Least Squares Mapping and a Fuzzy Neural Network , 2012, Sensors.

[7]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

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

[9]  Gerald Penn,et al.  Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  Suraj Prakash Harsha,et al.  Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN , 2013, Expert Syst. Appl..

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

[12]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[13]  Bo-Suk Yang,et al.  Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors , 2007, Expert Syst. Appl..

[14]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[15]  Yongbo Li,et al.  A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree , 2016 .

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

[17]  Fan Yang,et al.  Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker , 2011 .

[18]  김종영.  구글 TensorFlow 소개 , 2015 .

[19]  Steven Verstockt,et al.  Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .

[20]  Andrés Bustillo,et al.  Identifying maximum imbalance in datasets for fault diagnosis of gearboxes , 2018, J. Intell. Manuf..

[21]  Xiang Wang,et al.  Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding , 2015, Sensors.

[22]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[23]  Andrés Bustillo,et al.  An SVM-Based Solution for Fault Detection in Wind Turbines , 2015, Sensors.

[24]  Liang Chen,et al.  Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis , 2016 .

[25]  Jiaying Liu,et al.  Revisiting Batch Normalization For Practical Domain Adaptation , 2016, ICLR.

[26]  Xiaodong Cui,et al.  Data augmentation for deep convolutional neural network acoustic modeling , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[27]  Giansalvo Cirrincione,et al.  Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks , 2013, IEEE Transactions on Industrial Electronics.

[28]  P. Konar,et al.  Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) , 2011, Appl. Soft Comput..

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

[30]  V. Rai,et al.  Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform , 2007 .

[31]  Wonhee Lee,et al.  Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA , 2014, Sensors.

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

[33]  Moncef Gabbouj,et al.  Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks , 2017 .

[34]  José R. Perán,et al.  Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation , 2011 .

[35]  K. I. Ramachandran,et al.  Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN) , 2010, Expert Syst. Appl..

[36]  S. Joe Qin,et al.  Multivariate process monitoring and fault diagnosis by multi-scale PCA , 2002 .

[37]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[38]  Konstantinos C. Gryllias,et al.  Rolling element bearing fault detection in industrial environments based on a K-means clustering approach , 2011, Expert Syst. Appl..

[39]  Ahmad Ghasemloonia,et al.  Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis , 2011, Expert Syst. Appl..