Detection of ice states from mechanical vibrations using entropy measurements and machine learning algorithms

• Autorzy: Mejía Juan C. , Quintero Héctor F. , Echeverry-Correa Julián D. , Romero Carlos A.

Identyfikatory

DOI

10.29354/diag/130611

• Języki publikacji: EN

Abstrakty

EN

Entropy measurements are an accessible tool to perform irregularity and uncertainty measurements present in time series. Particularly in the area of signal processing, Multiscale Permutation Entropy (MPE) is presented as a characterization methodology capable of measuring randomness and non-linear dynamics present in non-stationary signals, such as mechanical vibrations. In this article, we present a robust methodology based on MPE for detection of Internal Combustion Engine (ICE) states. The MPE is combined with Principal Component Analysis (PCA) as a technique for visualization and feature selection and KNearest Neighbors (KNN) as a supervised classifier. The proposed methodology is validated by comparing accuracy and computation time with others presented in the literature. The results allow to appreciate a high effectiveness in the detection of failures in bearings (experiment 1) and ICE states (experiment 2) with a low computational consumption.

Słowa kluczowe

EN

entropy; vibration; dynamics; permutation; signal processing

PL

entropia; drgania; dynamika; permutacja; przetwarzanie sygnałów

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2020
• Tom: Vol. 21, No. 4
• Strony: 87--94
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Mejía Juan C.

• Universidad Tecnológica de Pereira

autor

Quintero Héctor F.

• Universidad Tecnológica de Pereira

autor

Echeverry-Correa Julián D.

• Universidad Tecnológica de Pereira

autor

Romero Carlos A.

• Universidad Tecnológica de Pereira

Bibliografia

• 1. Zheng P, Yang H. Generalized composite multiscale permutation entropy and laplacian score based rolling bearing fault diagnosis. Mechanical Systems and Signal Processing. 2018; 99: 229-243. https://doi.org/10.1016/j.ymssp.2017.06.011
• 2. Tiwari R, Gupta V, Kankar PK. Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier. Journal of Vibration and Control. 2015; 21(3): 461-467. https://doi.org/10.1177/1077546313490778
• 3. Martin H, Honarvar F. Application of statistical moments to bearing failure detection. Appl. Acoust. 1995; 44: 67-77. https://doi.org/10.1016/0003-682X(94)P4420-B
• 4. Heng RBW, Nor MJM. Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition. Applied Acoustics. 1998; 53: 211-226. https://doi.org/10.1016/S0003-682X(97)00018-2
• 5. Wei Z, Wang Y, Shuilong H. A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection. Knowledge-Based Systems. 2017; 116:1-12. https://doi.org/10.1016/j.knosys.2016.10.022
• 6. Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. American journal of physiology. Heart and circulatory physiology. 2000; 278: 39-49. https://doi.org/10.1152/ajpheart.2000.278.6.H2039
• 7. Pincus M. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences. 1991; 88(6): 2297-2301. https://doi.org/10.1073/pnas.88.6.2297
• 8. Costa M, Goldberger A, Chung P. Multiscale entropy analysis of physiologic time series. Physical Review Letters. 2001; 89: 220-232. https://doi.org/10.1103/PhysRevLett.89.068102
• 9. Bandt C, Pompe B. Permutation entropy: A natural complexity measure for time series. Physical Review Letters. 2002; 88:74-102. https://doi.org/10.1103/PhysRevLett.88.174102
• 10. Aziz W, Arif M. Multiscale permutation entropy of physiological time series. 2005 Pakistan Section Multitopic Conference, Karachi. 2005; 53:1-6, 2005. https://doi.org/10.1109/inmic.2005.334494
• 11. Zeng K, Ouyang G. Characterizing dynamics of absence seizure EEG with spatial-temporal permutation entropy. Neurocomputing. 2018; 275: 577-585. https://doi.org/10.1016/j.neucom.2017.09.007
• 12. Quintero H, López J. Monitoreo de vibraciones en maquinaria industrial. En: Vibraciones mecánicas un enfoque teórico-práctico, capitulo. 2016; 5, 294-298, 1st ed: Universidad Tecnológica de Pereira.
• 13. Daza S, Arias J, Llorente J. Dynamic feature extraction: an application to voice pathology detection. Intelligent Automation and Soft Computing. 2009; 15: 667-682. https://doi.org/10.1080/10798587.2009.10643056
• 14. Zhang X, Liang Y, Zhou J, Zang Y. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement. 2015; 69: 164-179. https://doi.org/10.1016/j.measurement.2015.03.017
• 15. Zheng J, Cheng J, Yang Y. A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy. Mechanism and Machine Theory. 2013;70:441-453. https://doi.org/10.1016/j.mechmachtheory.2013.08.01 4
• 16. Minghong H, Jiali P. A fault diagnosis method combined with LMD, sample entropy and energy ratio for roller bearings. Measurement. 2015;76:7-19. https://doi.org/10.1016/j.measurement.2015.08.019
• 17. Yuwono M, Qin Y, Zhou J. Automatic bearing fault diagnosis using particle swarm clustering and hidden Markov model. Engineering Applications of Artificial Intelligence. 2016; 47: 88-100. https://doi.org/10.1016/j.engappai.2015.03.007
• 18. Muruganatham B, Krishnakumar S, Murty S. Roller element bearing fault diagnosis using singular spectrum analysis. Mechanical Systems and Signal Processing. 2013; 35(1): 150-166. https://doi.org/10.1016/j.ymssp.2012.08.019
• 19. Jaouherl B, Fnaiech N, Saidi L. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics. 2015; 89:16-27. https://doi.org/10.1016/j.apacoust.2014.08.016
• 20. William P, Hoffman M. Identification of bearing faults using time domain zero-crossings. Mechanical Systems and Signal Processing. 2011;25(8):3078-3088. https://doi.org/10.1016/j.ymssp.2011.06.001
• 21. Liang L, Liu F, Li M. Feature selection for machine fault diagnosis using clustering of non-negation matrix factorization. Measurement 2016; 94: 295-305. https://doi.org/10.1016/j.measurement.2016.08.003
• 22. Vencalek 0, Pokotylo O. Depth-weighted bayes classification. Computational Statistics Data Analysis. 2018; 123: 1-12. https://doi.org/10.1016/j.csda.2018.01.011
• 23. Penny B, Geoff J. Classification trees for poverty mapping. Computational Statistics Data Analysis. 2017; 115:53-66. https://doi.org/10.1016/j.csda.2017.05.009
• 24. Yinhe C, Wen T, Jue G. Detecting dynamical changes in time series using the permutation entropy. Phys. Rev. E. 2004;70:174-195. https://doi.org/10.1103/PhysRevE.70.046217
• 25. Philipp M, Katri S, Ville N. Scent classification by k nearest neighbors using ion-mobility spectrometry measurements. Expert Systems with Applications. 2018;15:199-210. https://doi.org/10.1016/j.eswa.2018.08.042
• 26. Bearing Data Center. https://csegroups.case.edu/bearingdatacenter/home
• 27. Nayana R, Geethanjali P. Analysis of statistical timedomain features effectiveness in identification of bearing faults from vibration signal. IEEE Sensors Journal. 2017; 17:5618-5625. https://doi.org/10.1109/JSEN.2017.2727638
• 28. Keheng Z, Xigeng S, Dongxin X. A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm. Measurement. 2014;47:669-675. https://doi.org/10.1016/j.measurement.2013.09.019
• 29. Zhiwen L, Hongrui C, Xuefeng C. Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings. Neurocomputing. 2013; 99: 399-410. https://doi.org/10.1016/j.neucom.2012.07.019
• 30. Ocak H, Loparo K. HMM-based fault detection and diagnosis scheme for rolling element bearings. Journal of Vibration and Acoustics. 2005; 127: 2-15. https://doi.org/10.1115/1.1924636
• 31. Shao H, Jiang H, Wang F, Zhao H. An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowledge-Based Systems. 2017; 119:200-220. https://doi.org/10.1016/j.knosys.2016.12.012

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).