A Novel Bearing Condition Monitoring Method in Induction Motors Based on Instantaneous Frequency of Motor Voltage

Bearing failures are the most frequent faults in induction motors. By utilizing an efficient bearing condition monitoring (BCM) method, unscheduled breakdowns and unnecessary costs caused by bearing failures can be eliminated. The methods proposed in the literature for this purpose are mainly based on measuring and analyzing vibration and current. This paper proposes a novel BCM method based on “instantaneous frequency of motor voltage.” Simulation and experimental results show that defective bearings cause to increase the amount of non-Gaussianity in this signal. In this paper, it is shown the global kurtosis of the proposed signal is able to detect bearing defects at an early stage. In contrast, for both vibration and current signals, it is preferable to applying the kurtosis locally in different frequency bands. The electrical signals are acquired from the motor terminals and motor control center of an oil pump station. Results approve the merits and effectiveness of the proposed method for industrial applications.

[1]  Claude Delpha,et al.  Improved Fault Diagnosis of Ball Bearings Based on the Global Spectrum of Vibration Signals , 2015, IEEE Transactions on Energy Conversion.

[2]  A. Carlosena,et al.  Instrument for the measurement of the instantaneous frequency , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[3]  Michael J. Devaney,et al.  Adjustable Speed Drive Bearing Fault Detection via Wavelet Packet Decomposition , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[4]  Bhim Singh,et al.  Investigation of Vibration Signatures for Multiple Fault Diagnosis in Variable Frequency Drives Using Complex Wavelets , 2014, IEEE Transactions on Power Electronics.

[5]  Gérard-André Capolino,et al.  Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art , 2015, IEEE Transactions on Industrial Electronics.

[6]  Ruoyu Li,et al.  Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach , 2013, IEEE Transactions on Industrial Electronics.

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

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

[9]  Thomas G. Habetler,et al.  An amplitude modulation detector for fault diagnosis in rolling element bearings , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[10]  Myeongsu Kang,et al.  An FPGA-Based Multicore System for Real-Time Bearing Fault Diagnosis Using Ultrasampling Rate AE Signals , 2015, IEEE Transactions on Industrial Electronics.

[11]  Aarnout Brombacher,et al.  Probability... , 2009, Qual. Reliab. Eng. Int..

[12]  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.

[13]  Humberto Henao,et al.  Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques , 2014, IEEE Industrial Electronics Magazine.

[14]  A. T. Johns,et al.  Frequency relaying based on instantaneous frequency measurement [power systems] , 1996 .

[15]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[16]  Ezio Bassi,et al.  Stator Current and Motor Efficiency as Indicators for Different Types of Bearing Faults in Induction Motors , 2010, IEEE Transactions on Industrial Electronics.

[17]  Wenbin Wang,et al.  Economic Analysis of Canary-Based Prognostics and Health Management , 2011, IEEE Transactions on Industrial Electronics.

[18]  Erik Leandro Bonaldi,et al.  Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current , 2015, IEEE Transactions on Industrial Electronics.

[19]  Roger F. Dwyer,et al.  Detection of non-Gaussian signals by frequency domain Kurtosis estimation , 1983, ICASSP.

[20]  Mohd Jailani Mohd Nor,et al.  Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition , 1998 .

[21]  Lorand Szabo,et al.  Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement , 2015, IEEE Transactions on Industrial Electronics.

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

[23]  Baptiste Trajin,et al.  Comparison Between Stator Current and Estimated Mechanical Speed for the Detection of Bearing Wear in Asynchronous Drives , 2009, IEEE Transactions on Industrial Electronics.

[24]  Iqbal Gondal,et al.  Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach , 2015, IEEE Transactions on Industrial Electronics.

[25]  Myeongsu Kang,et al.  Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis , 2015, IEEE Transactions on Power Electronics.

[26]  Hubert Razik,et al.  Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique , 2013, IEEE Transactions on Industrial Electronics.

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

[28]  Peter Vas,et al.  Sensorless vector and direct torque control , 1998 .

[29]  J. Antoni.  The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

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

[31]  Thomas W. Rauber,et al.  Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[32]  François Guillet,et al.  A New Bearing Fault Detection Method in Induction Machines Based on Instantaneous Power Factor , 2008, IEEE Transactions on Industrial Electronics.

[33]  Selin Aviyente,et al.  Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings , 2015, IEEE Transactions on Industrial Electronics.

[34]  Antje Winkel.  Vector Control And Dynamics Of Ac Drives , 2016 .

[35]  V. Eckhardt,et al.  Dynamic measuring of frequency and frequency oscillations in multiphase power systems , 1989 .

[36]  Giansalvo Cirrincione,et al.  Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks , 2013, IEEE Transactions on Industrial Electronics.

[37]  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.

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