Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network

Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

[1]  Yu Yang,et al.  A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM , 2007 .

[2]  Diego Cabrera,et al.  Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning , 2016, Sensors.

[3]  Qingsong Xu,et al.  Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis , 2015, Neural Computing and Applications.

[4]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches , 2015, IEEE Transactions on Industrial Electronics.

[5]  Chen Lu,et al.  Fault diagnosis for rolling bearing based on SIFT-KPCA and SVM , 2017 .

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

[7]  Derek C. Rose,et al.  Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier] , 2010, IEEE Computational Intelligence Magazine.

[8]  Huaqing Wang,et al.  Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR , 2010, Sensors.

[9]  Seungchul Lee,et al.  Rotating Machinery Diagnostics Using Deep Learning on Orbit Plot Images , 2016 .

[10]  Pingfeng Wang,et al.  Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..

[11]  Shixi Yang,et al.  A new procedure for extracting fault feature of multi-frequency signal from rotating machinery , 2012 .

[12]  Yi-Qing Ni,et al.  Wind pressure data reconstruction using neural network techniques: A comparison between BPNN and GRNN , 2016 .

[13]  Carlo Santulli,et al.  Failure modes characterization of impacted carbon fibre reinforced plastics laminates under compression loading using acoustic emission , 2014 .

[14]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[15]  Amy Loutfi,et al.  A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..

[16]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

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

[18]  Andrew D. Ball,et al.  An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks , 2014, Expert Syst. Appl..

[19]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[20]  Jay Lee,et al.  Information Reconstruction Method for Improved Clustering and Diagnosis of Generic Gearbox Signals , 2011 .

[21]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[22]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition , 2012 .

[23]  Jiong Li,et al.  A novel efficient SVM-based fault diagnosis method for multi-split air conditioning system's refrigerant charge fault amount , 2016 .

[24]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[25]  Chuan Li,et al.  Enhancement of the Wear Particle Monitoring Capability of Oil Debris Sensors Using a Maximal Overlap Discrete Wavelet Transform with Optimal Decomposition Depth , 2014, Sensors.

[26]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble , 2007 .

[27]  Hao Tian,et al.  A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox , 2011, Expert Syst. Appl..

[28]  Jian Hou,et al.  Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes , 2016, Neurocomputing.

[29]  P. D. McFadden,et al.  A technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox , 1991 .

[30]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[31]  Don-Ha Hwang,et al.  Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals , 2015 .

[32]  V. Sugumaran,et al.  Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump , 2013 .

[33]  Geoffrey E. Hinton,et al.  Unsupervised Learning of Image Transformations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  David Siegel,et al.  A Systematic Methodology for Gearbox Health Assessment and Fault Classification , 2009 .

[35]  Justin A. Blanco,et al.  Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement , 2011, Journal of neural engineering.

[36]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[37]  Chandrasekhar Nataraj,et al.  Use of particle swarm optimization for machinery fault detection , 2009, Eng. Appl. Artif. Intell..

[38]  Lin Zhang,et al.  Research on Fault Diagnosis Method Based on Rule Base Neural Network , 2017, J. Control. Sci. Eng..

[39]  Cunbao Ma,et al.  Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis , 2017, Neurocomputing.

[40]  Wei Pan,et al.  Unsupervised hierarchical modeling of locomotion styles , 2009, ICML '09.

[41]  Haidong Shao,et al.  A novel deep autoencoder feature learning method for rotating machinery fault diagnosis , 2017 .

[42]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[43]  Geoffrey E. Hinton,et al.  Learning a better representation of speech soundwaves using restricted boltzmann machines , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[44]  Chu Kiong Loo,et al.  Thermal condition monitoring system using log-polar mapping, quaternion correlation and max-product fuzzy neural network classification , 2010, Neurocomputing.

[45]  Amy Loutfi,et al.  Sleep Stage Classification Using Unsupervised Feature Learning , 2012, Adv. Artif. Neural Syst..

[46]  Bo-Suk Yang,et al.  Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference , 2009, Expert Syst. Appl..

[47]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[48]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  Hee-Jun Kang,et al.  Rolling element bearing fault diagnosis based on non-local means de-noising and empirical mode decomposition , 2014 .

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

[51]  Yaguo Lei,et al.  A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes , 2012, Sensors.