Vibration Analysis Based Interturn Fault Diagnosis in Induction Machines

A vibration analysis based interturn fault diagnosis of induction machines is proposed in this paper, using a neural-network-based scheme, constituting of two parts. The first part finds out the optimum network size of the probabilistic neural network (PNN) using the Orthogonal Least Squares Regression algorithm. This judges the size of the PNN, with an effort to reduce the computation. The feature extraction to model the PNN is made meaningful using dual tree complex wavelet transform (DTCWT), which is nearly shift invariant analytical wavelet transform, giving a true representation of the input space. In the second part, preprocessing using principal component analysis is suggested as an effective way to further reduce the dimension of the feature set and size of the PNN without compromising the performance. The sensitivity, specificity, and accuracy show that the vibration signatures capture the fault more effectively (especially by the axial and radial ones), under varying supply-frequency and load conditions. A comparison with traditional discrete wavelet transform proves the applicability of the proposed scheme. A comparative evaluation with feedforward neural network and naïve Bayes scheme brings out the advantage of the proposed optimized DTCWT-PNN based technique over other machine learning approaches.

[1]  Nicola Bianchi,et al.  Experimental Tests of Dual Three-Phase Induction Motor Under Faulty Operating Condition , 2012, IEEE Transactions on Industrial Electronics.

[2]  Chuanyi Ji,et al.  Performance and efficiency: recent advances in supervised learning , 1999, Proc. IEEE.

[3]  Mo-Yuen Chow,et al.  Multiple Discriminant Analysis and Neural-Network-Based Monolith and Partition Fault-Detection Schemes for Broken Rotor Bar in Induction Motors , 2006, IEEE Transactions on Industrial Electronics.

[4]  Antonio J. Marques Cardoso,et al.  Diagnosis of stator inter-turn short circuits in DTC induction motor drives , 2003 .

[5]  Zhiwei Gao,et al.  Descriptor observer approaches for multivariable systems with measurement noises and application in fault detection and diagnosis , 2006, Syst. Control. Lett..

[6]  Nagarajan Murali,et al.  Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference , 2013, IEEE Transactions on Industrial Electronics.

[7]  B. Lohmann,et al.  A computational method to robust vibration control of vehicle engine-body system using Haar wavelets , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[8]  Fabrice Druaux,et al.  Self adaptive growing neural network classifier for faults detection and diagnosis , 2011, Neurocomputing.

[9]  Daniel W. C. Ho,et al.  State/noise estimator for descriptor systems with application to sensor fault diagnosis , 2006, IEEE Transactions on Signal Processing.

[10]  Eric Blanco,et al.  Coupling Pattern Recognition With State Estimation Using Kalman Filter for Fault Diagnosis , 2012, IEEE Transactions on Industrial Electronics.

[11]  Vincent Choqueuse,et al.  Diagnosis of Three-Phase Electrical Machines Using Multidimensional Demodulation Techniques , 2012, IEEE Transactions on Industrial Electronics.

[12]  Luis Romeral,et al.  Modeling of Surface-Mounted Permanent Magnet Synchronous Motors With Stator Winding Interturn Faults , 2011, IEEE Transactions on Industrial Electronics.

[13]  T.G. Habetler,et al.  Nonstationary Motor Fault Detection Using Recent Quadratic Time–Frequency Representations , 2006, IEEE Transactions on Industry Applications.

[14]  T. G. Habetler,et al.  Stator current harmonics and their causal vibrations: a preliminary investigation of sensorless vibration monitoring applications , 1999 .

[15]  Luis Romeral,et al.  Motor Fault Detection Using a Rogowski Sensor Without an Integrator , 2009, IEEE Transactions on Industrial Electronics.

[16]  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.

[17]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[18]  T.G. Habetler,et al.  A Survey of Methods for Detection of Stator-Related Faults in Induction Machines , 2007, IEEE Transactions on Industry Applications.

[19]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[20]  Hubert Razik,et al.  A Novel Monitoring of Load Level and Broken Bar Fault Severity Applied to Squirrel-Cage Induction Motors Using a Genetic Algorithm , 2009, IEEE Transactions on Industrial Electronics.

[21]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .

[22]  M. Haji,et al.  Pattern Recognition-A Technique for Induction Machines Rotor Broken Bar Detection , 2001, IEEE Power Engineering Review.

[23]  Jay A. Farrell,et al.  Wavelet-based system identification for nonlinear control , 1999, IEEE Trans. Autom. Control..

[24]  A.J.M. Cardoso,et al.  Diagnosis of Rotor Faults in Closed-Loop Induction Motor Drives , 2006, Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting.

[25]  Kil To Chong,et al.  Induction Machine Condition Monitoring Using Neural Network Modeling , 2007, IEEE Transactions on Industrial Electronics.

[26]  Seungdeog Choi,et al.  Performance-Oriented Electric Motors Diagnostics in Modern Energy Conversion Systems , 2012, IEEE Transactions on Industrial Electronics.

[27]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[28]  Leila Parsa,et al.  Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors , 2011, IEEE Transactions on Industrial Electronics.

[29]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[30]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[31]  Wee Ser,et al.  Probabilistic neural-network structure determination for pattern classification , 2000, IEEE Trans. Neural Networks Learn. Syst..

[32]  N. Kingsbury.  Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[33]  M. Riera-Guasp,et al.  A Critical Comparison Between DWT and Hilbert–Huang-Based Methods for the Diagnosis of Rotor Bar Failures in Induction Machines , 2009, IEEE Transactions on Industry Applications.