Detecting generalized roughness faults in synchronous-generator bearings using the energy in the current spectrum

Many synchronous generator failures are caused by degradation initiated by the onset of generalized roughness in bearings. This paper demonstrates an approach for detecting the existence of generalized-roughness conditions in operational generators. The method tracks the energy contained in the stator current. It has been tested in the laboratory using a 5kW, 480V synchronous generator. Generalized-roughness faults are produced in that setup by injecting shaft currents into the machine so that failures naturally develop during operation. An accelerated failure mechanism is described, and experimental results are presented.

[1]  Mansour Ojaghi,et al.  Winding function approach to simulate induction motors under sleeve bearing fault , 2014, 2014 IEEE International Conference on Industrial Technology (ICIT).

[2]  Steven B. Leeb,et al.  Nonintrusive Load Monitoring and Diagnostics in Power Systems , 2008, IEEE Transactions on Instrumentation and Measurement.

[3]  H. Prashad Investigation of damaged rolling-element bearings and deterioration of lubricants under the influence of electric fields , 1994 .

[4]  H.A. Toliyat,et al.  Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review , 2005, IEEE Transactions on Energy Conversion.

[5]  M. E. H. Benbouzid,et al.  What Stator Current Processing Based Technique to Use for Induction Motor Rotor Faults Diagnosis , 2002, IEEE Power Engineering Review.

[6]  Kumar Verma Alok,et al.  Oil whip detection using stator current monitoring , 2012 .

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

[8]  Steven R. Shaw,et al.  A Kalman-Filter Spectral Envelope Preprocessor , 2007, IEEE Transactions on Instrumentation and Measurement.

[9]  R. Harley,et al.  Bearing fault detection via autoregressive stator current modeling , 2004, IEEE Transactions on Industry Applications.

[10]  Changhee Cho,et al.  Monitoring of journal bearing faults based on motor current signature analysis for induction motors , 2015, 2015 IEEE Energy Conversion Congress and Exposition (ECCE).

[11]  Peter Vas,et al.  Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines , 1993 .

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

[13]  Thomas G. Habetler,et al.  Experimentally generating faults in rolling element bearings via shaft current , 2003, 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003..

[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]  T.G. Habetler,et al.  Motor bearing damage detection using stator current monitoring , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.