A non invasive fault diagnosis system for induction motors in noisy environment

In this paper a phase detection method for fault diagnosis of the induction motors has been presented. The proposed method has a powerful environmental noise suppression capability. It has been shown in literature that the performance of the previously used fault detection method (instantaneous power analysis) was affected by the environmental noise, switching disturbances and other low order harmonics. The instantaneous power analysis yields erroneous results under low load conditions of the motor where fault signature was buried in the noise. It has been theoretically and experimentally shown that the proposed phase detection method can detect fault signatures in the noisy environment without use of any extra hardware. The accuracy of the proposed phase detection method was compared with the instantaneous power analysis method for bearing ball defects and the results on the real hardware implementation confirm the effectiveness of the proposed approach.

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

[2]  D.N. Walker,et al.  Torsional Vibration and Fatigue of Turbine-Generator shafts , 1981, IEEE Transactions on Power Apparatus and Systems.

[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]  Chenglin Gu,et al.  Study of broken bars in three‐phase squirrel‐cage induction motors at standstill , 2013 .

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

[6]  Jawad Faiz,et al.  Diagnosis methods for stator winding faults in three‐phase squirrel‐cage induction motors , 2014 .

[7]  Rene de Jesus Romero-Troncoso,et al.  Real‐time condition monitoring on VSD‐fed induction motors through statistical analysis and synchronous speed observation , 2015 .

[8]  T.G. Habetler,et al.  Experimentally generating faults in rolling element bearings via shaft current , 2005, IEEE Transactions on Industry Applications.

[9]  T.G. Habetler,et al.  Effects of machine speed on the development and detection of rolling element bearing faults , 2003, IEEE Power Electronics Letters.

[10]  Jordi-Roger Riba Ruiz,et al.  On-line fault detection method for induction machines based on signal convolution , 2011 .

[11]  Vijanth S. Asirvadam,et al.  An Intelligent Diagnostic Condition Monitoring System for AC Motors via Instantaneous Power Analysis , 2013 .

[12]  O Gol,et al.  Condition Monitoring of Electrical Machines , 1986 .

[13]  Peter Tavner,et al.  Condition monitoring of wind turbine induction generators with rotor electrical asymmetry , 2012 .

[14]  Shahin Hedayati Kia,et al.  A High-Resolution Frequency Estimation Method for Three-Phase Induction Machine Fault Detection , 2007, IEEE Transactions on Industrial Electronics.

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

[16]  Peter Tavner,et al.  Review of condition monitoring of rotating electrical machines , 2008 .

[17]  P. G. McLaren,et al.  Transient thermal characteristics of induction machine rotor cage , 1988 .

[18]  Bertrand Raison,et al.  Models for bearing damage detection in induction motors using stator current monitoring , 2008, 2004 IEEE International Symposium on Industrial Electronics.

[19]  Seungdeog Choi,et al.  Implementation of a Fault-Diagnosis Algorithm for Induction Machines Based on Advanced Digital-Signal-Processing Techniques , 2011, IEEE Transactions on Industrial Electronics.

[20]  Vijanth S. Asirvadam,et al.  An intelligent diagnostic system for the condition monitoring of AC motors , 2013, 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA).

[21]  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.

[22]  Wei Zhou,et al.  Bearing Condition Monitoring Methods for Electric Machines: A General Review , 2007, 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.

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

[24]  S. F. Legowski,et al.  Diagnostics of mechanical abnormalities in induction motors using instantaneous electric power , 1997, 1997 IEEE International Electric Machines and Drives Conference Record.

[25]  M.E.H. Benbouzid,et al.  Induction motor asymmetrical faults detection using advanced signal processing techniques , 1999 .

[26]  Bong-Hwan Kwon,et al.  Online Diagnosis of Induction Motors Using MCSA , 2006, IEEE Transactions on Industrial Electronics.

[27]  Sinisa Djurovic,et al.  Wound rotor induction generator bearing fault modelling and detection using stator current analysis , 2013 .

[28]  Intesar Ahmed,et al.  Investigation of single and multiple faults under varying load conditions using multiple sensor types to improve condition monitoring of induction machines. , 2008 .

[29]  W. T. Thomson,et al.  Current signature analysis to detect induction motor faults , 2001 .

[30]  Thomas G. Habetler,et al.  Evaluation and implementation of a system to eliminate arbitrary load effects in current-based monitoring of induction machines , 1996, IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting.

[31]  Mohamed Benbouzid,et al.  Induction motors' faults detection and localization using stator current advanced signal processing techniques , 1999 .

[32]  Wei Zhou,et al.  Bearing Fault Detection Via Stator Current Noise Cancellation and Statistical Control , 2008, IEEE Transactions on Industrial Electronics.

[33]  B. C. Nakra,et al.  Comparison of vibration and acoustic measurement techniques for the condition monitoring of rolling element bearings , 1992 .

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