Fault diagnostic system based on approximate reasoning
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Published
Mar 26, 2021
Pavle Boškoski
Bojan Musizza
Janko Petrovčić
Ðani Juričić
How to Cite
Boškoski, P., Musizza , B. ., Petrovčić, J. ., & Juričić, Ðani . (2021). Fault diagnostic system based on approximate reasoning. Annual Conference of the PHM Society, 1(1). Retrieved from http://papers.phmsociety.org/index.php/phmconf/article/view/1576
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Keywords
fault diagnosis, feature extraction, possibilistic methods
References
(Antoni and Randall, 2006) J. Antoni and R.B. Ran- dall. The spectral kurtosis: a useful tool for charac- terisingnon-stationarysignals.MechanicalSystems and Signal Processing, 20:282–307, 2006.
(Antoni, 2006) J. Antoni. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, 20:308–331, 2006.
(Antoni, 2007) J. Antoni. Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions. Journal of Sound and Vibration, 304:497 – 529, 2007.
(Benko et al., 2005) U. Benko, J. Petrovčić ́, D. Juričić ́, J. Tavcǎr, and J. Rejec. An approach to fault diagnosis of a vacuum cleaner motors based on sound analysis. Mechanical Systems and Signal Processing, 19:427 – 445, 2005.
(Didier et al., 2007) G. Didier, E. Ternisien, O. Cas- pary, and H. Razik. A new approach to detect broken rotor bars in induction machines by current spectrum analysis. Mechanical Systems and Signal Processing, 21:1127–1142, 2007.
(Dwyer, 1983) R. Dwyer. Detection of non-gaussian signals by frequency domain kurtosis estimation. Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP, 8:607–610, 1983.
(Ho and Randall, 2000) D. Ho and R. B. Randall. Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechan- ical Systems and Signal Processing, 14:763–788, 2000.
(Isermann and Ballé, 1997) R. Isermann and P. Ballé. Trends in the application of mode-based fault detection and diagnosis of technical processes. Control Engineering Practice, 5:709–719, 1997.
(Isermann, 2000) R. Isermann. Issues of fault diagnosis for dynamic systems, chapter Integration of Fault Detection and Diagnosis Methods, pages 15– 50. Springer, 2000.
(Jack and Nandi, 2001) L. B. Jack and A. K. Nandi. Fault detection using support vector machines and artificial neural networks augmented by genetic al- gorithms. Mechanical Systems and Signal Processing, 16:373–390, 2001.
(Jardine et al., 2006) A.K.S. Jardine, D. Lin, and D. Banjevič. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20:1483–1510, 2006.
(Juričić ́ et al., 2001) D. Juričić ́, O. Moseler, and A. Rakar. Model-based condition monitoring of an actuator system driven by a brushless dc motor. Control Engineering Practice, 9:545–554, 2001.
(Kar and Mohanty, 2006) C. Kar and A. R. Mohanty. Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechan- ical Systems and Signal Processing, 20:158–187, 2006.
(Leia et al., 2008) Yaguo Leia, Zhengjia Hea, Yanyang Zia, and Xuefeng Chena. New clustering algorithm-based fault diagnosis using compensation distance evaluation technique. Mechanical Systems and Signal Processing, 22:419–435, 2008.
(McFadden and Smith, 1984) P.D. McFadden and J.D. Smith. Vibration monitoring of rolling element bearings by the high-frequency resonance technique - a review. Tribology International, 17:3–10, 1984.
(Rakar and Juričić ́, 2002) A. Rakar and Ð. JJuričić ́. Di- agnostic reasoning under conflicting data: the application of the transferable belief model. Journal of Process Control, 12:55–67, 2002.
(Randall et al., 2001) R. B. Randall, J. Antoni, and S. Chobsaard. The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mechanical Systems and Signal Processing, 15:945 – 962, 2001.
(Röpke and Filbert, 1994) K. Röpke and D. Filbert. Unsupervised classification of universal motors using modern clustering algorithms. In Proc. SAFE- PROCESS’94, IFAC Symp. on Fault Detection, Supervision and Technical Processes, II:720–725, 1994.
(Rubini and Meneghetti, 2001) R. Rubini and U. Meneghetti. Application of the envelope and wavelet transform and analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing, 15:287–302, 2001.
(Samanta, 2004) B. Samanta. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing, 18:625–644, 2004.
(Sasi et al., 2001) B. Sasi, A.. Payne, B. York, A.. Gu, and F. Ball. Condition monitoring of electric motors using instantaneous angular speed. In paper presented at the Maintenance and Reliability Conference (MARCON), Gatlinburg, TN,, 2001.
(Sawalhi et al., 2007) N. Sawalhi, R.B. Randall, and H. Endo. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mechanical Systems and Signal Processing, 21:2616–2633, 2007.
(Singh and Kazzaz, 2009) G.K. Singh and S. A. Kaz- zaz. Isolation and identification of dry bearing faults in induction machine using wavelet transform. Tribology International, 42:849–861, 2009.
(Smets and Kennes, 1994) P. Smets and R. Kennes. The transferable belief model. Artificial Intelligence, 66:191–234, 1994.
(Staszewski, 1998) W. J. Staszewski. Wavelet based compression and feature selection for vibration analysis. Journal of Sound and Vibration, 211:735 – 760, 1998.
(Tandon and Choudhury, 1999) N. Tandon and A. Choudhury. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32:469–480, 1999.
(Tinta et al., 2005) D. Tinta, J. Petrovcic, U. Benko,Ð. Juričić, A. Rakar, M. Žele, J. Tavcǎr, J. Re- jec, and A. Stefanovska. Fault diagnosis of vacuum cleaner motors. Control Engineering Practice, 13:177–187, 2005.
(Wang, 2001) Wenyi Wang. Early detection of gear tooth cracking using the resonance demodulation technique. Mechanical Systems and Signal Processing, 15:887–903, 2001.
(Xu and Marangoni, 1994a) M. Xu and R.D. Marangoni. Vibration analysis of a motor-flexible coupling-rotor system subject to misalignment and unbalance, part i: Theoretical model and analyses. Journal of Sound and Vibration, 176:663–679, 1994.
(Xu and Marangoni, 1994b) M. Xu and R.D. Marangoni. Vibration analysis of a motor-flexible coupling-rotor system subject to misalignment and unbalance, part ii: Experimental validation. Journal of Sound and Vibration, 176:681–691, 1994.
(Zarei and Poshtan, 2007) J. Zarei and J. Poshtan. Bearing fault detection using wavelet packet transform of induction motor stator current. Tribology International, 40:763–769, 2007.
(Antoni, 2006) J. Antoni. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, 20:308–331, 2006.
(Antoni, 2007) J. Antoni. Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions. Journal of Sound and Vibration, 304:497 – 529, 2007.
(Benko et al., 2005) U. Benko, J. Petrovčić ́, D. Juričić ́, J. Tavcǎr, and J. Rejec. An approach to fault diagnosis of a vacuum cleaner motors based on sound analysis. Mechanical Systems and Signal Processing, 19:427 – 445, 2005.
(Didier et al., 2007) G. Didier, E. Ternisien, O. Cas- pary, and H. Razik. A new approach to detect broken rotor bars in induction machines by current spectrum analysis. Mechanical Systems and Signal Processing, 21:1127–1142, 2007.
(Dwyer, 1983) R. Dwyer. Detection of non-gaussian signals by frequency domain kurtosis estimation. Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP, 8:607–610, 1983.
(Ho and Randall, 2000) D. Ho and R. B. Randall. Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechan- ical Systems and Signal Processing, 14:763–788, 2000.
(Isermann and Ballé, 1997) R. Isermann and P. Ballé. Trends in the application of mode-based fault detection and diagnosis of technical processes. Control Engineering Practice, 5:709–719, 1997.
(Isermann, 2000) R. Isermann. Issues of fault diagnosis for dynamic systems, chapter Integration of Fault Detection and Diagnosis Methods, pages 15– 50. Springer, 2000.
(Jack and Nandi, 2001) L. B. Jack and A. K. Nandi. Fault detection using support vector machines and artificial neural networks augmented by genetic al- gorithms. Mechanical Systems and Signal Processing, 16:373–390, 2001.
(Jardine et al., 2006) A.K.S. Jardine, D. Lin, and D. Banjevič. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20:1483–1510, 2006.
(Juričić ́ et al., 2001) D. Juričić ́, O. Moseler, and A. Rakar. Model-based condition monitoring of an actuator system driven by a brushless dc motor. Control Engineering Practice, 9:545–554, 2001.
(Kar and Mohanty, 2006) C. Kar and A. R. Mohanty. Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechan- ical Systems and Signal Processing, 20:158–187, 2006.
(Leia et al., 2008) Yaguo Leia, Zhengjia Hea, Yanyang Zia, and Xuefeng Chena. New clustering algorithm-based fault diagnosis using compensation distance evaluation technique. Mechanical Systems and Signal Processing, 22:419–435, 2008.
(McFadden and Smith, 1984) P.D. McFadden and J.D. Smith. Vibration monitoring of rolling element bearings by the high-frequency resonance technique - a review. Tribology International, 17:3–10, 1984.
(Rakar and Juričić ́, 2002) A. Rakar and Ð. JJuričić ́. Di- agnostic reasoning under conflicting data: the application of the transferable belief model. Journal of Process Control, 12:55–67, 2002.
(Randall et al., 2001) R. B. Randall, J. Antoni, and S. Chobsaard. The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mechanical Systems and Signal Processing, 15:945 – 962, 2001.
(Röpke and Filbert, 1994) K. Röpke and D. Filbert. Unsupervised classification of universal motors using modern clustering algorithms. In Proc. SAFE- PROCESS’94, IFAC Symp. on Fault Detection, Supervision and Technical Processes, II:720–725, 1994.
(Rubini and Meneghetti, 2001) R. Rubini and U. Meneghetti. Application of the envelope and wavelet transform and analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing, 15:287–302, 2001.
(Samanta, 2004) B. Samanta. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing, 18:625–644, 2004.
(Sasi et al., 2001) B. Sasi, A.. Payne, B. York, A.. Gu, and F. Ball. Condition monitoring of electric motors using instantaneous angular speed. In paper presented at the Maintenance and Reliability Conference (MARCON), Gatlinburg, TN,, 2001.
(Sawalhi et al., 2007) N. Sawalhi, R.B. Randall, and H. Endo. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mechanical Systems and Signal Processing, 21:2616–2633, 2007.
(Singh and Kazzaz, 2009) G.K. Singh and S. A. Kaz- zaz. Isolation and identification of dry bearing faults in induction machine using wavelet transform. Tribology International, 42:849–861, 2009.
(Smets and Kennes, 1994) P. Smets and R. Kennes. The transferable belief model. Artificial Intelligence, 66:191–234, 1994.
(Staszewski, 1998) W. J. Staszewski. Wavelet based compression and feature selection for vibration analysis. Journal of Sound and Vibration, 211:735 – 760, 1998.
(Tandon and Choudhury, 1999) N. Tandon and A. Choudhury. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32:469–480, 1999.
(Tinta et al., 2005) D. Tinta, J. Petrovcic, U. Benko,Ð. Juričić, A. Rakar, M. Žele, J. Tavcǎr, J. Re- jec, and A. Stefanovska. Fault diagnosis of vacuum cleaner motors. Control Engineering Practice, 13:177–187, 2005.
(Wang, 2001) Wenyi Wang. Early detection of gear tooth cracking using the resonance demodulation technique. Mechanical Systems and Signal Processing, 15:887–903, 2001.
(Xu and Marangoni, 1994a) M. Xu and R.D. Marangoni. Vibration analysis of a motor-flexible coupling-rotor system subject to misalignment and unbalance, part i: Theoretical model and analyses. Journal of Sound and Vibration, 176:663–679, 1994.
(Xu and Marangoni, 1994b) M. Xu and R.D. Marangoni. Vibration analysis of a motor-flexible coupling-rotor system subject to misalignment and unbalance, part ii: Experimental validation. Journal of Sound and Vibration, 176:681–691, 1994.
(Zarei and Poshtan, 2007) J. Zarei and J. Poshtan. Bearing fault detection using wavelet packet transform of induction motor stator current. Tribology International, 40:763–769, 2007.
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Technical Research Papers
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