Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

Abstract Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

[1]  Yaguo Lei,et al.  Condition monitoring and fault diagnosis of planetary gearboxes: A review , 2014 .

[2]  Peter W. Tse,et al.  A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects , 2014, Sensors.

[3]  James Hensman,et al.  Natural computing for mechanical systems research: A tutorial overview , 2011 .

[4]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs , 2007 .

[5]  Fanrang Kong,et al.  Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier , 2013 .

[6]  Yoshua Bengio,et al.  Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.

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

[8]  Jeroen van den Brink,et al.  The quantum nature of skyrmions and half-skyrmions in Cu2OSeO3 , 2014, Nature Communications.

[9]  S.A.V. Satya Murty,et al.  Roller element bearing fault diagnosis using singular spectrum analysis , 2013 .

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

[11]  Peter W. Tse,et al.  Classification of gear faults using cumulants and the radial basis function network , 2004 .

[12]  Rasmus Berg Palm,et al.  Prediction as a candidate for learning deep hierarchical models of data , 2012 .

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

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

[15]  Balbir S. Dhillon,et al.  Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network , 2012 .

[16]  Peter W. Tse,et al.  Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis , 2011 .

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

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

[19]  Dong Wang,et al.  Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition , 2009 .

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

[21]  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..

[22]  Changqing Shen,et al.  A novel adaptive wavelet stripping algorithm for extracting the transients caused by bearing localized faults , 2013 .

[23]  Jong-Duk Son,et al.  Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine , 2009, Expert Syst. Appl..

[24]  Geoffrey E. Hinton,et al.  Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.

[25]  B. Samanta,et al.  Artificial neural networks and genetic algorithms for gear fault detection , 2004 .

[26]  Li Deng,et al.  A tutorial survey of architectures, algorithms, and applications for deep learning , 2014, APSIPA Transactions on Signal and Information Processing.

[27]  Yan Song Wang,et al.  A sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network , 2014 .

[28]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

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

[30]  M. H. Mathias,et al.  Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron , 2015 .

[31]  Guolin He,et al.  Semisupervised Distance-Preserving Self-Organizing Map for Machine-Defect Detection and Classification , 2013, IEEE Transactions on Instrumentation and Measurement.

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

[33]  P. Baldi,et al.  Searching for exotic particles in high-energy physics with deep learning , 2014, Nature Communications.

[34]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

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

[36]  Peter W. Tse,et al.  Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method , 2015 .

[37]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[38]  Ling Shao,et al.  Image Blur Classification and Parameter Identification Using Two-stage Deep Belief Networks , 2013, BMVC.

[39]  Fengshou Gu,et al.  Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis , 2013 .