Detection and Estimation of Extremely Small Fault Signature by Utilizing Multiple Current Sensor Signals in Electric Machines

This paper presents a novel motor current signature analysis algorithm for accurate detection and precise estimation of extremely small fault signature by utilizing multiple–phase-current signals in an electric machine. The two most critical challenges in developing a reliable diagnosis method are the detection of extreme small signals under extremely harsh environment, and the formulation of adaptive threshold under dynamic operation and noise variation of a motor. These challenges are addressed in this paper by logically and statistically utilizing multiple existing current sensors and current signals to make an accurate detection, and statistical/adaptive threshold for precise detection and decision making. The performance of the proposed algorithm has been mathematically and theoretically proved under modeled Gaussian noise conditions. Proposed theory is also experimentally verified on a 3-kW five-phase permanent-magnet-assisted synchronous reluctance motor.

[1]  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.

[2]  Jin Hur,et al.  Comparison of the Fault Characteristics of IPM-Type and SPM-Type BLDC Motors Under Inter-Turn Fault Conditions Using Winding Function Theory , 2014, IEEE Transactions on Industry Applications.

[3]  H. A. Toliyat,et al.  Performance Analysis of a Three-Phase Induction Motor under Mixed Eccentricity Condition , 2002, IEEE Power Engineering Review.

[4]  H.A. Toliyat,et al.  A generalized condition monitoring method for multi-phase induction motors , 2009, 2009 IEEE International Electric Machines and Drives Conference.

[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]  Seungdeog Choi,et al.  Fault detection of a five-phase permanent magnet synchronous reluctance motor based on symmetrical components theory , 2015, 2015 IEEE International Electric Machines & Drives Conference (IEMDC).

[7]  Hamid Reza Karimi,et al.  Fault diagnosis of induction motors broken rotor bars by pattern recognition based on noise cancelation , 2014, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).

[8]  H.A. Toliyat,et al.  DSP implementation of the multiple reference frames theory for the diagnosis of stator faults in a DTC induction motor drive , 2003, IEEE Transactions on Energy Conversion.

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

[10]  Hamid A. Toliyat,et al.  Multiphase induction motor drives - : a technology status review , 2007 .

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

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

[13]  S. Nandi,et al.  Detection of Eccentricity Faults in Three-Phase Reluctance Synchronous Motor , 2012, IEEE Transactions on Industry Applications.

[14]  C. Gerada,et al.  The results do mesh , 2007, IEEE Industry Applications Magazine.

[15]  Michael Nowak,et al.  An adaptive noise-cancellation method for detecting generalized roughness bearing faults under dynamic load conditions , 2009, 2009 IEEE Energy Conversion Congress and Exposition.

[16]  Gérard-André Capolino,et al.  Advances in Diagnostic Techniques for Induction Machines , 2008, IEEE Transactions on Industrial Electronics.

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

[18]  Jawad Faiz,et al.  Static-, Dynamic-, and Mixed-Eccentricity Fault Diagnoses in Permanent-Magnet Synchronous Motors , 2009, IEEE Transactions on Industrial Electronics.

[19]  Hamid A. Toliyat,et al.  Phase-Sensitive Detection of Motor Fault Signatures in the Presence of Noise , 2008, IEEE Transactions on Industrial Electronics.

[20]  Jie Liu,et al.  An Extended Wavelet Spectrum for Bearing Fault Diagnostics , 2008, IEEE Transactions on Instrumentation and Measurement.

[21]  Jin Hur,et al.  Detection of Inter-Turn and Dynamic Eccentricity Faults Using Stator Current Frequency Pattern in IPM-Type BLDC Motors , 2016, IEEE Transactions on Industrial Electronics.

[22]  H. Guzman,et al.  A comprehensive fault analysis of a five-phase induction motor drive with an open phase , 2012, 2012 15th International Power Electronics and Motion Control Conference (EPE/PEMC).

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

[24]  Gérard-André Capolino,et al.  Modeling and Control of Six-Phase Symmetrical Induction Machine Under Fault Condition Due to Open Phases , 2008, IEEE Transactions on Industrial Electronics.

[25]  Ramin Salehi Arashloo,et al.  Fault Detection and Fault Tolerant Operation of a Five Phase PM Motor Drive Using Adaptive Model Identification Approach , 2014, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[26]  Sailes K. Sengijpta.  Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

[27]  Andrew J. Viterbi,et al.  Principles of coherent communication , 1966 .

[28]  Alberto Bellini,et al.  Diagnosis of Induction Machines' Rotor Faults in Time-Varying Conditions , 2009, IEEE Transactions on Industrial Electronics.