Fault Diagnosis of Bearing Damage by Means of the Linear Discriminant Analysis of Stator Current Features From the Frequency Selection

Bearing damage is the most common failure in electrical machines. It can be detected by vibration analysis. However, this diagnosis method is costly or not always accessible due to the location of the equipment and the choice of the implemented sensors. An alternative method is provided with the electrical monitoring using the stator current of the electrical machine. This study aims at developing a diagnostic system based on the current feature generated by a frequency selection in the stator current spectrum. The features are evaluated by means of the linear discriminant analysis and the fault diagnosis is performed with the Bayes classifier. The proposed method is evaluated by two types of damages at different load cases. The results show that the damaged bearings can be distinguished from the healthy bearing depending on the considered load cases.

[1]  T.G. Habetler,et al.  Fault classification and fault signature production for rolling element bearings in electric machines , 2004, IEEE Transactions on Industry Applications.

[2]  Pascal Maussion,et al.  Stator current based indicators for bearing fault detection in synchronous machine by statistical frequency selection , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[3]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[4]  Kay Hameyer,et al.  Impact of the load in the detection of bearing faults by using the stator current in PMSM's , 2014, 2014 International Conference on Electrical Machines (ICEM).

[5]  T.G. Habetler,et al.  Incipient Bearing Fault Detection via Motor Stator Current Noise Cancellation Using Wiener Filter , 2009, IEEE Transactions on Industry Applications.

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

[7]  Daniel Morinigo-Sotelo,et al.  Supervised diagnosis of induction motor faults: A proposed methodology for an improved performance evaluation , 2015, 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED).

[8]  Report of Large Motor Reliability Survey of Industrial and Commercial Installations, Part I , 1985, IEEE Transactions on Industry Applications.

[9]  R. R. Obaid,et al.  Stator current analysis for bearing damage detection in induction motors , 2003, 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003..

[10]  H. Razik,et al.  Fault detection and diagnosis of induction motors based on hidden Markov model , 2012, 2012 XXth International Conference on Electrical Machines.

[11]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[12]  Kay Hameyer,et al.  Untersuchung von Bauteilschäden elektrischer Antriebsstränge im Belastungsprüfstand mittels Statorstromanalyse , 2012 .

[13]  A.J.M. Cardoso,et al.  Bearing failures diagnosis in three-phase induction motors by extended Park's vector approach , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[14]  A. Garcia,et al.  A novel condition monitoring scheme for bearing faults based on Curvilinear Component Analysis and hierarchical neural networks , 2012, 2012 XXth International Conference on Electrical Machines.

[15]  L. Romeral,et al.  Fault Detection in dynamic conditions by means of Discrete Wavelet Decomposition for PMSM running under Bearing Damage , 2009, 2009 Twenty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition.

[16]  Bertrand Raison,et al.  Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring , 2004, IEEE Transactions on Industrial Electronics.

[17]  Thomas G. Habetler,et al.  An unsupervised, on-line system for induction motor fault detection using stator current monitoring , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[18]  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.

[19]  Peter Tavner,et al.  Condition Monitoring of Rotating Electrical Machines , 2008 .

[20]  S. Poignant,et al.  Bearing fault detection in synchronous machine based on the statistical analysis of stator current , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.