Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis

Considering various health conditions under varying operational conditions, the mining sensitive feature from the measured signals is still a great challenge for intelligent fault diagnosis of spindle bearings. This paper proposed a novel energy-fluctuated multiscale feature mining approach based on wavelet packet energy (WPE) image and deep convolutional network (ConvNet) for spindle bearing fault diagnosis. Different from the vector characteristics applied in intelligent diagnosis of spindle bearings, wavelet packet transform is first combined with phase space reconstruction to rebuild a 2-D WPE image of the frequency subspaces. This special image can reconstruct the local relationship of the WP nodes and hold the energy fluctuation of the measured signal. Then, the identifiable characteristics can be further learned by a special architecture of the deep ConvNet. Other than the traditional neural network architecture, to maintain the global and local information simultaneously, deep ConvNet combines the skipping layer with the last convolutional layer as the input of the multiscale layer. The comparisons of clustering distribution and classification accuracy with six other features show that the proposed feature mining approach is quite suitable for spindle bearing fault diagnosis with multiclass classification regardless of the load fluctuation.

[1]  Bing He,et al.  Feature fusion using kernel joint approximate diagonalization of eigen-matrices for rolling bearing fault identification , 2016 .

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

[3]  H. W. Ngan,et al.  Detection of Motor Bearing Outer Raceway Defect by Wavelet Packet Transformed Motor Current Signature Analysis , 2010, IEEE Transactions on Instrumentation and Measurement.

[4]  Noureddine Zerhouni,et al.  Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.

[5]  K. I. Ramachandran,et al.  Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing , 2007 .

[6]  Yang Xiao,et al.  Fault Diagnosis Using a Joint Model Based on Sparse Representation and SVM , 2016, IEEE Transactions on Instrumentation and Measurement.

[7]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[8]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Robert X. Gao,et al.  PCA-based feature selection scheme for machine defect classification , 2004, IEEE Transactions on Instrumentation and Measurement.

[10]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[11]  Qingbo He.  Vibration signal classification by wavelet packet energy flow manifold learning , 2013 .

[12]  Erich Elsen,et al.  Deep Speech: Scaling up end-to-end speech recognition , 2014, ArXiv.

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

[14]  Stéphane Mallat,et al.  A Wavelet Tour of Signal Processing, 2nd Edition , 1999 .

[15]  N. Zerhouni,et al.  Fault prognostic of bearings by using support vector data description , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[16]  Hee-Jun Kang,et al.  Wavelet Kernel Local Fisher Discriminant Analysis With Particle Swarm Optimization Algorithm for Bearing Defect Classification , 2015, IEEE Transactions on Instrumentation and Measurement.

[17]  Yann LeCun,et al.  Traffic sign recognition with multi-scale Convolutional Networks , 2011, The 2011 International Joint Conference on Neural Networks.

[18]  Yichuang Sun,et al.  A New Neural-Network-Based Fault Diagnosis Approach for Analog Circuits by Using Kurtosis and Entropy as a Preprocessor , 2010, IEEE Transactions on Instrumentation and Measurement.

[19]  Cong Wang,et al.  Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings , 2016 .

[20]  Xiaogang Wang,et al.  Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Luca Maria Gambardella,et al.  Flexible, High Performance Convolutional Neural Networks for Image Classification , 2011, IJCAI.

[22]  Yuning Jiang,et al.  Learning Deep Face Representation , 2014, ArXiv.

[23]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[24]  Ruqiang Yan,et al.  A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .

[25]  Qingbo He,et al.  A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification , 2015 .

[26]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .