Motor fault detection method for vibration signal using FFT residuals

Induction motors play an important role in the safe and efficient operation of industrial plants because they are considered inherently reliable due to their robust and relatively simple design. Therefore, there is an indispensable need to monitor their health and performance. And the detection and diagnosis of motor faults is the base to improve the efficiency of industrial plants. In this paper, a residual model-based fault detection method is proposed for steady state vibration signals of induction motors. The stationary signal had been extracted from the entire signal using data segmentation. The reference model in spectra is obtained statistically to represent the healthy condition. The ratio of RMS of residuals is used as a fault indicator for detecting motor faults. The effectiveness of the proposed approach in detecting a wide range of mechanical faults is demonstrated through the experiments applied on an 800 hp motor, and it is shown that a robust and reliable induction motor fault detection system has been produced.

[1]  Rolf Isermann,et al.  Integration of Fault Detection and Diagnosis Methods , 1994 .

[2]  F. M. Atay,et al.  A novel method for fault detection in electrical motors , 2001 .

[3]  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).

[4]  Eugene I. Rivin,et al.  Vibration Of Bearings , 1989 .

[5]  Jie Chen,et al.  Observer-based fault detection and isolation: robustness and applications , 1997 .

[6]  Peter Tavner,et al.  Condition monitoring of electrical machines , 1987 .

[7]  A. G. Parlos,et al.  MODEL-BASED FAULT DIAGNOSIS OF INDUCTION MOTORS USING NON-STATIONARY SIGNAL SEGMENTATION , 2002 .

[8]  R. J. Patton,et al.  Sensor fault-tolerant control of a rail traction drive , 1999 .

[9]  Alexander G. Parlos,et al.  Sensorless fault diagnosis of induction motors , 2003, IEEE Trans. Ind. Electron..

[10]  T. G. Habetler,et al.  A method for sensorless on-line vibration monitoring of induction machines , 1997, IAS '97. Conference Record of the 1997 IEEE Industry Applications Conference Thirty-Second IAS Annual Meeting.

[11]  S. Jangi,et al.  Embedding spectral analysis in equipment , 1991, IEEE Spectrum.

[12]  Alexander G. Parlos,et al.  Reducing the Impact of False Alarms in Induction Motor Fault , 2003 .

[13]  S. Poyhonen,et al.  INDEPENDENT COMPONENT ANALYSIS OF VIBRATIONS FOR FAULT DIAGNOSIS OF AN INDUCTION MOTOR , 2003 .

[14]  J. Penman,et al.  Inverter fed induction machine condition monitoring using the bispectrum , 1997, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics.

[15]  Jie Chen,et al.  Robustness in quantitative model-based fault diagnosis , 1992 .

[16]  D. J. Petty.  Analysis of bearing vibration monitoring , 1999 .

[17]  Károly Sólyomvári,et al.  Vibration Testing of Ma-chines and Their Maintenance , 1990 .