Principal component analysis anomaly detector for rotor broken bars

In this article a method for the detection of broken rotor bars in asynchronous machines operating under full load is presented. Unlike most Motor Current Signature Analysis (MCSA) approaches, which operate in the frequency domain, our method operates in the time domain. The scheme is based on the use of a Principal Component Analysis (PCA) fault/anomaly detector. PCA is applied on the three stator currents to subsequently calculate the Q statistic which is employed for detecting the presence/absence of a fault. The efficiency of the proposed scheme was experimentally evaluated using different fault severity levels, ranging from 1/4 of a broken bar to three broken bars. The obtained results indicate that the method can detect the caused asymmetry with a very restricted amount of data.

[1]  Sameer Singh,et al.  Novelty detection: a review - part 1: statistical approaches , 2003, Signal Process..

[2]  G.E. Dawson,et al.  The detection of broken bars in the cage rotor of an induction machine , 1988, Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting.

[3]  Austin H. Bonnett,et al.  Rotor Failures in Squirrel Cage Induction Motors , 1986, IEEE Transactions on Industry Applications.

[4]  Chrysostomos D. Stylios,et al.  Automatic Pattern Identification Based on the Complex Empirical Mode Decomposition of the Startup Current for the Diagnosis of Rotor Asymmetries in Asynchronous Machines , 2014, IEEE Transactions on Industrial Electronics.

[5]  V. K. Giri,et al.  Broken rotor bar fault detection in induction motors using Wavelet Transform , 2012, 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET).

[6]  Norman Mariun,et al.  Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review , 2011 .

[7]  M. Riera-Guasp,et al.  Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines , 2006, IEEE Transactions on Industry Applications.

[8]  Thomas G. Habetler,et al.  A survey of condition monitoring and protection methods for medium voltage induction motors , 2009 .

[9]  Da Ruan,et al.  Intelligent Data Mining: Techniques and Applications , 2005, Studies in Computational Intelligence.

[10]  O. Ondel,et al.  A method to detect broken bars in induction machine using pattern recognition techniques , 2006, IEEE Transactions on Industry Applications.

[11]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[12]  V. Fernao Pires,et al.  Eigenvector/eigenvalue analysis of a 3D current referential fault detection and diagnosis of an induction motor , 2010 .

[13]  Thomas Gustafsson,et al.  Experimental evaluation of a broken rotor bar fault detection scheme based on Uncertainty Bounds violation , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[14]  Mohamed Benbouzid,et al.  A review of induction motors signature analysis as a medium for faults detection , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[15]  A.H. Bonnett,et al.  Increased Efficiency Versus Increased Reliability , 2008, IEEE Industry Applications Magazine.

[16]  George Nikolakopoulos,et al.  A Fault diagnosis scheme for three phase induction motors based on uncertainty bounds , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[17]  Zhe Zhang,et al.  Online rotor mixed fault diagnosis way based on spectrum analysis of instantaneous power in squirrel cage induction motors , 2004 .

[18]  Hubert Razik,et al.  Induction Motor Diagnosis Using Line Neutral Voltage Signatures , 2009, IEEE Transactions on Industrial Electronics.

[19]  Chris Aldrich,et al.  Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods , 2013, Advances in Computer Vision and Pattern Recognition.

[20]  R. Alves,et al.  Analysis of Air Gap Flux to Detect Induction Motor Faults , 2006, Proceedings of the 41st International Universities Power Engineering Conference.

[21]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[22]  Richard J. Povinelli,et al.  Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors , 2013, IEEE Transactions on Industrial Informatics.

[23]  Kesari Verma,et al.  Intelligent Data Mining Techniques For Coal Mining Data , 2013 .

[24]  Janos Gertler,et al.  Fault Detection and Diagnosis , 2008, Encyclopedia of Systems and Control.

[25]  George Nikolakopoulos,et al.  Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines , 2013, Expert Syst. Appl..

[26]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[27]  Angel R. Martinez,et al.  : Exploratory data analysis with MATLAB ® , 2007 .

[28]  W. T. Thomson,et al.  Current And Vibration Monitoring For Fault Diagnosis And Root Cause Analysis Of Induction Motor Drives. , 2002 .

[29]  Luis Angel García-Escudero,et al.  Robust condition monitoring for early detection of broken rotor bars in induction motors , 2011, Expert Syst. Appl..

[30]  George Nikolakopoulos,et al.  Broken bars fault diagnosis based on uncertainty bounds violation for three‐phase induction motors , 2015 .

[31]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[32]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .