An Enhanced Trace Ratio Linear Discriminant Analysis for Fault Diagnosis: An Illustrated Example Using HDD Data

This paper proposes a pattern recognition method for the fault diagnosis of machinery based on an enhanced trace ratio linear discriminant analysis (ETR-LDA) algorithm. It is an extension of TR-LDA that pursues to maximize the between-class distance and minimize the within-class distance. Since TR-LDA focuses on the total between-class distance and ignores the attention to local features, it may lead to deficiencies in the classification of specific patterns/classes, which is defined as short board in this paper. To cope with this limitation, the proposed ETR-LDA makes a reconsideration of the relationship between the total between-class distance and the global separability. More specifically, a new objective function is derived in ETR-LDA by taking the smallest between-class distance into account. The optimal solution of this objective function is proved to improve the smallest between-class distance with no change to the convergence and the global optimality of the algorithm, and the separability of different classes is increased eventually. Both synthetic data and hard disk drive wearing experimental data are employed to verify the efficiency of the proposed method. The results show that ETR-LDA is able to distinguish those fault categories and outperform TR-LDA and other dimensionality reduction methods.

[1]  Orestes Llanes-Santiago,et al.  Principal components selection for dimensionality reduction using discriminant information applied to fault diagnosis , 2015 .

[2]  Ping Zhang,et al.  On the application of PCA technique to fault diagnosis , 2010 .

[3]  Gérard-André Capolino,et al.  Advances in Diagnostic Techniques for Induction Machines , 2008, IEEE Transactions on Industrial Electronics.

[4]  Hamid A. Toliyat,et al.  Condition monitoring and fault diagnosis of electrical machines-a review , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[5]  Tommy W. S. Chow,et al.  Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis , 2014, IEEE Transactions on Industrial Electronics.

[6]  Ruoyu Li,et al.  Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification , 2012, IEEE Transactions on Instrumentation and Measurement.

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

[8]  Ioannis Antoniadis,et al.  Rolling element bearing fault diagnosis using wavelet packets , 2002 .

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

[10]  Tommy W. S. Chow,et al.  ITR-Score algorithm: An efficient Trace ratio criterion based algorithm for supervised dimensionality reduction , 2011, The 2011 International Joint Conference on Neural Networks.

[11]  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.

[12]  L. Barford,et al.  Signal interpretation for monitoring and diagnosis, a cooling system testbed , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[13]  G. Rong,et al.  Generalized orthogonal locality preserving projections for nonlinear fault detection and diagnosis , 2009 .

[14]  Jafar Zarei,et al.  Induction motors bearing fault detection using pattern recognition techniques , 2012, Expert Syst. Appl..

[15]  Seungdeog Choi,et al.  A Simple Real-Time Fault Signature Monitoring Tool for Motor-Drive-Embedded Fault Diagnosis Systems , 2011, IEEE Transactions on Industrial Electronics.

[16]  Long Zhang,et al.  Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference , 2010, Expert Syst. Appl..

[17]  Dong Xu,et al.  Trace Ratio vs. Ratio Trace for Dimensionality Reduction , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Baoping Tang,et al.  Fault diagnosis method based on incremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier , 2014 .

[19]  Venkat Venkatasubramanian,et al.  PCA-SDG based process monitoring and fault diagnosis , 1999 .

[20]  Yanyang Zi,et al.  Fault diagnosis from visualization perspective using stream statistics , 2016, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[21]  Yaguo Lei,et al.  Application of an intelligent classification method to mechanical fault diagnosis , 2009, Expert Syst. Appl..

[22]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[23]  Tommy W. S. Chow,et al.  A Two-Step Parametric Method for Failure Prediction in Hard Disk Drives , 2014, IEEE Transactions on Industrial Informatics.

[24]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[25]  Wei Chen,et al.  Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization , 2015, Neurocomputing.

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

[27]  Prem Kumar Kalra,et al.  Audio Signature-Based Condition Monitoring of Internal Combustion Engine Using FFT and Correlation Approach , 2011, IEEE Transactions on Instrumentation and Measurement.

[28]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[29]  Alberto Bellini,et al.  Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals , 2009, IEEE Transactions on Industrial Electronics.

[30]  Tommy W. S. Chow,et al.  Trace Ratio Optimization-Based Semi-Supervised Nonlinear Dimensionality Reduction for Marginal Manifold Visualization , 2013, IEEE Transactions on Knowledge and Data Engineering.

[31]  Wei Zhou,et al.  Bearing Fault Detection Via Stator Current Noise Cancellation and Statistical Control , 2008, IEEE Transactions on Industrial Electronics.

[32]  Jin Chen,et al.  Decision tree and PCA-based fault diagnosis of rotating machinery , 2007 .

[33]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[34]  Yaguo Lei,et al.  New clustering algorithm-based fault diagnosis using compensation distance evaluation technique , 2008 .

[35]  Feiping Nie,et al.  Trace Ratio Problem Revisited , 2009, IEEE Transactions on Neural Networks.

[36]  Hongkai Jiang,et al.  An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis , 2013 .

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

[38]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  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.

[40]  Quansheng Jiang,et al.  Machinery fault diagnosis using supervised manifold learning , 2009 .

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

[42]  Xu Fei-yun.  Method of Fault Pattern Recognition Based on Laplacian Eigenmaps , 2008 .