The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines.

A critical work of bearing fault diagnosis is locating the optimum frequency band that contains faulty bearing signal, which is usually buried in the noise background. Now, envelope analysis is commonly used to obtain the bearing defect harmonics from the envelope signal spectrum analysis and has shown fine results in identifying incipient failures occurring in the different parts of a bearing. However, the main step in implementing envelope analysis is to determine a frequency band that contains faulty bearing signal component with the highest signal noise level. Conventionally, the choice of the band is made by manual spectrum comparison via identifying the resonance frequency where the largest change occurred. In this paper, we present a squared envelope based spectral kurtosis method to determine optimum envelope analysis parameters including the filtering band and center frequency through a short time Fourier transform. We have verified the potential of the spectral kurtosis diagnostic strategy in performance improvements for single-defect diagnosis using real laboratory-collected vibration data sets.

[1]  Brigitte Chebel-Morello,et al.  Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis , 2014, SoCPaR.

[2]  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.

[3]  Luigi Garibaldi,et al.  Spectral Kurtosis against SVM for best frequency selection in bearing diagnostics , 2010 .

[4]  Farhat Fnaiech,et al.  Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis , 2014, 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[5]  Chanan Singh,et al.  Report of Large Motor Reliability Survey of Industrial and Commercial Installations, Part II , 1985, IEEE Transactions on Industry Applications.

[6]  M. Dalva,et al.  A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals and oil refineries , 1994, Proceedings of IEEE Petroleum and Chemical Industry Technical Conference (PCIC '94).

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

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

[9]  Tom Fawcett,et al.  Using rule sets to maximize ROC performance , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[10]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[11]  Qingwei Gao,et al.  A detection method for bearing faults using null space pursuit and S transform , 2014, Signal Process..

[12]  Tomasz Barszcz,et al.  A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .

[13]  Brigitte Chebel-Morello,et al.  Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .

[14]  Arturo Garcia-Perez,et al.  The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors , 2011, IEEE Transactions on Industrial Electronics.

[15]  F. Fnaiech,et al.  Stator current bi-spectrum patterns for induction machines multiple-faults detection , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[16]  Naim Baydar,et al.  A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution. , 2001 .

[17]  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.

[18]  Paolo Pennacchi,et al.  Testing second order cyclostationarity in the squared envelope spectrum of non-white vibration signals , 2013 .

[19]  Robert B. Randall,et al.  The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis , 2007 .

[20]  Paolo Pennacchi,et al.  The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings , 2014 .

[21]  F. Fnaiech,et al.  Bearing defects decision making using higher order spectra features and support vector machines , 2013, 14th International Conference on Sciences and Techniques of Automatic Control & Computer Engineering - STA'2013.

[22]  Tomasz Barszcz,et al.  Fractal Based Signal Processing for Fault Detection in Ball-Bearings , 2012 .

[23]  J. Antoni Fast computation of the kurtogram for the detection of transient faults , 2007 .

[24]  Robert B. Randall,et al.  Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .

[25]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[26]  Jérôme Antoni,et al.  The infogram: Entropic evidence of the signature of repetitive transients , 2016 .

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

[28]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[29]  Robert B. Randall,et al.  Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram , 2009 .

[30]  Jing Na,et al.  Envelope order tracking for fault detection in rolling element bearings , 2012 .

[31]  Jérôme Antoni,et al.  Integrated modulation intensity distribution as a practical tool for condition monitoring , 2014 .

[32]  Jose A. Antonino-Daviu,et al.  Scale Invariant Feature Extraction Algorithm for the Automatic Diagnosis of Rotor Asymmetries in Induction Motors , 2013, IEEE Transactions on Industrial Informatics.

[33]  M. Feldman Hilbert transform in vibration analysis , 2011 .

[34]  Peter W. Tse,et al.  Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition , 2012 .

[35]  Farhat Fnaiech,et al.  Application of higher order spectral features and support vector machines for bearing faults classification. , 2015, ISA transactions.

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

[37]  T.G. Habetler,et al.  Fault-signature modeling and detection of inner-race bearing faults , 2006, IEEE Transactions on Industry Applications.

[38]  Gaël Chevallier,et al.  Tracking and removing modulated sinusoidal components: A solution based on the kurtosis and the Extended , 2013 .