Degradation assessment of bearing based on machine learning classification matrix

• Autorzy: Kumar Satish , Kumar Paras , Kumar Girish

Identyfikatory

DOI

10.17531/ein.2021.2.20

• Języki publikacji: EN

Abstrakty

EN

In the broad framework of degradation assessment of bearing, the final objectives of bearing condition monitoring is to evaluate different degradation states and to estimate the quantitative analysis of degree of performance degradation. Machine learning classification matrices have been used to train models based on health data and real time feedback. Diagnostic and prognostic models based on data driven perspective have been used in the prior research work to improve the bearing degradation assessment. Industry 4.0 has required the research in advanced diagnostic and prognostic algorithm to enhance the accuracy of models. A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model. Review work demonstrates the comparisons among the available state-of-the-art methods. In the end, unexplored research technical challenges and niches of opportunity for future researchers are discussed.

Słowa kluczowe

EN

degradation state; health condition indicator; machine learning; diagnostic model; prognostic model

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2021
• Tom: Vol. 23, no. 2
• Strony: 395--404
• Opis fizyczny: Bibliogr. 65 poz., rys., tab.

Twórcy

autor

Kumar Satish

• Delhi Technological University, Delhi, India

autor

Kumar Paras

• Delhi Technological University, Delhi, India

autor

Kumar Girish

• Delhi Technological University, Delhi, India

Bibliografia

• 1. Abboud D, Elbadaoui M, Smith W A, Randall R B. Advanced bearing diagnostics: A comparative study of two powerful approaches. Mechanical Systems and Signal Processing 2019; 114: 604-627, https://doi.org/10.1016/j.ymssp.2018.05.011.
• 2. Ahmad W, Khan S A, Islam M M M, Kim J M. A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models. Reliability Engineering and System Safety 2019; 184: 67-76, https://doi.org/10.1016/j.ress.2018.02.003.
• 3. Ben Ali J, Chebel-Morello B, Saidi L et al. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing 2015; 56: 150-172, https://doi.org/10.1016/j.ymssp.2014.10.014.
• 4. Ben Ali J, Fnaiech N, Saidi L et al. 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.
• 5. Andrzejczak K, Selech J. Generalised gamma distribution in the corrective maintenance prediction of homogeneous vehicles. Lecture Notes in Networks and Systems 2019; 68: 519-529, https://doi.org/10.1007/978-3-030-12450-2_50.
• 6. Antoni J, Borghesani P. A statistical methodology for the design of condition indicators. Mechanical Systems and Signal Processing 2019;114: 290-327, https://doi.org/10.1016/j.ymssp.2018.05.012.
• 7. Boškoski P, Gašperin M, Petelin D, Juričić D. Bearing fault prognostics using Rényi entropy based features and Gaussian process models. Mechanical Systems and Signal Processing 2015; 52-53(1): 327-337, https://doi.org/10.1016/j.ymssp.2014.07.011.
• 8. Chen L, Hang Y. Performance degradation assessment and fault diagnosis of bearing based on EMD and PCA-SOM. Vibroengineering Procedia 2013; 2: 12-16.
• 9. Chen Y, Peng G, Zhu Z, Li S. A novel deep learning method based on attention mechanism for bearing remaining useful life prediction. Applied Soft Computing Journal 2020; 86: 105919, https://doi.org/10.1016/j.asoc.2019.105919.
• 10. Diez-Olivan A, Del Ser J, Galar D, Sierra B. Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0. Information Fusion 2019; 50: 92-111, https://doi.org/10.1016/j.inffus.2018.10.005.
• 11. Dong S, Luo T. Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement: Journal of the International Measurement Confederation 2013; 46(9): 3143-3152, https://doi.org/10.1016/j.measurement.2013.06.038.
• 12. Dong S, Xu X, Chen R. Application of fuzzy C ‑ means method and classification model of optimized K ‑ nearest neighbor for fault diagnosis of bearing. Journal of the Brazilian Society of Mechanical Sciences and Engineering 2016; 38(8): 2255-2263, https://doi.org/10.1007/s40430-015-0455-9.
• 13. Gao J, Yang J, Huang D et al. Experimental application of vibrational resonance on bearing fault diagnosis. Journal of the Brazilian Society of Mechanical Sciences and Engineering 2019; 41(1): 1-13, https://doi.org/10.1007/s40430-018-1502-0.
• 14. He M, Zhou Y, Li Y et al. Long short-term memory network with multi-resolution singular value decomposition for prediction of bearing performance degradation. Measurement 2020; 156: 107582, https://doi.org/10.1016/j.measurement.2020.107582.
• 15. Heng W, Guangxian N, Jinhai C, Jiangming Q. Research on Rolling Bearing State Health Monitoring and Life Prediction Based on PCA and Internet of Things with Multi-sensor. Measurement 2020: 107657, https://doi.org/10.1016/j.measurement.2020.107657.
• 16. Heng W, Jinhai C, Jiangming Q, Guangxian N. A new approach for safety life prediction of industrial rolling bearing based on state recognition and similarity analysis. Safety Science 2020; 122:104530, https://doi.org/10.1016/j.ssci.2019.104530.
• 17. Hong S, Zhou Z, Zio E, Hong K. Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method. Digital Signal Processing: A Review Journal 2014; 27(1): 159-166, https://doi.org/10.1016/j.dsp.2013.12.010.
• 18. Janjarasjitt S, Ocak H, Loparo K A. Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal. Journal of Sound and Vibration 2008; 317(1-2): 112-126, https://doi.org/10.1016/j.jsv.2008.02.051.
• 19. Jardine A K S, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 2006; 20(7): 1483-1510, https://doi.org/10.1016/j.ymssp.2005.09.012.
• 20. Jasiulewicz-Kaczmarek M, Antosz K, Żywica P et al. Framework of machine criticality assessment with criteria interactions. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(2): 207-220, https://doi.org/10.17531/ein.2021.2.1.
• 21. Jeong H, Park S, Woo S, Lee S. Rotating Machinery Diagnostics Using Deep Learning on Orbit Plot Images. Procedia Manufacturing 2016; 5: 1107-1118, https://doi.org/10.1016/j.promfg.2016.08.083.
• 22. Jha M S, Dauphin-Tanguy G, Ould-Bouamama B. Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework. Mechanical Systems and Signal Processing 2016; 75: 301-329, https://doi.org/10.1016/j.ymssp.2016.01.010.
• 23. Kan M S, Tan A C C, Mathew J. A review on prognostic techniques for non-stationary and non-linear rotating systems. Mechanical Systems and Signal Processing 2015; 62: 1-20, https://doi.org/10.1016/j.ymssp.2015.02.016.
• 24. Khan S, Yairi T. A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing 2018; 107: 241-265, https://doi.org/10.1016/j.ymssp.2017.11.024.
• 25. Kim H E, Tan A C C, Mathew J, Choi B K. Bearing fault prognosis based on health state probability estimation. Expert Systems with Applications 2012; 39(5): 5200-5213, https://doi.org/10.1016/j.eswa.2011.11.019.
• 26. Kozłowski E, Mazurkiewicz D, Żabiński T et al. Machining sensor data management for operation-level predictive model. Expert Systems with Applications 2020; 159: 113600, https://doi.org/10.1016/j.eswa.2020.113600.
• 27. Lamine M, Abdelkrim F. Hybrid SOM - PCA method for modeling bearing faults detection and diagnosis. Journal of the Brazilian Society of Mechanical Sciences and Engineering 2018; 40(5): 1-8, https://doi.org/10.1007/s40430-018-1184-7.
• 28. Lee J, Wu F, Zhao W et al. Prognostics and health management design for rotary machinery systems - reviews, methodology and applications. Mechanical Systems and Signal Processing 2014; 42(1-2): 314-334, https://doi.org/10.1016/j.ymssp.2013.06.004.
• 29. Li Z, Fang H, Huang M et al. Data-driven bearing fault identification using improved hidden Markov model and self-organizing map. Computers and Industrial Engineering 2018; 116: 37-46, https://doi.org/10.1016/j.cie.2017.12.002.
• 30. Li Z, Wu D, Hu C, Terpenny J. An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction. Reliability Engineering and System Safety 2019; 184: 110-122, https://doi.org/10.1016/j.ress.2017.12.016.
• 31. Liang S Y, Li Y, Billington S A et al. Adaptive prognostics for rotary machineries. Procedia Engineering 2014; 86: 852-857, https://doi.org/10.1016/j.proeng.2014.11.106.
• 32. Loganathan M K, Gandhi O P. Reliability enhancement of manufacturing systems through functions. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2017; 231(10): 1850-1868, https://doi.org/10.1177/0954405415612324.
• 33. Loganathan M K, Gandhi O P. Reliability evaluation and analysis of CNC cam shaft grinding machine. Journal of Engineering, Design and Technology 2015; 13(1): 37-73, https://doi.org/10.1108/JEDT-10-2012-0042.
• 34. Madar E, Klein R, Bortman J. Contribution of dynamic modeling to prognostics of rotating machinery. Mechanical Systems and Signal Processing 2019; 123: 496-512, https://doi.org/10.1016/j.ymssp.2019.01.003.
• 35. Meng Z, Li J, Yin N, Pan Z. Remaining useful life prediction of rolling bearing using fractal theory. Measurement 2020; 156: 107572, https://doi.org/10.1016/j.measurement.2020.107572.
• 36. Miao Q, Zhang X, Liu Z, Zhang H. Condition multi-classification and evaluation of system degradation process using an improved support vector machine. Microelectronics Reliability 2017; 75: 223-232, https://doi.org/10.1016/j.microrel.2017.03.020.
• 37. Ni G, Chen J, Wang H. Degradation assessment of rolling bearing towards safety based on random matrix single ring machine learning. Safety Science 2019; 118(5): 403-408, https://doi.org/10.1016/j.ssci.2019.05.010.
• 38. Ocak H, Loparo K A, Discenzo F M. Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics. Journal of Sound and Vibration 2007; 302(4-5): 951-961, https://doi.org/10.1016/j.jsv.2007.01.001.
• 39. P. Varghese J, Kumar G. Availability Analysis with Opportunistic Maintenance of a Two Component Deteriorating System. International Journal of Materials, Mechanics and Manufacturing 2014; 2(2): 155-160, https://doi.org/10.7763/IJMMM.2014.V2.119.
• 40. Pan D, Liu J B, Cao J. Remaining useful life estimation using an inverse Gaussian degradation model. Neurocomputing 2016; 185: 64-72, https://doi.org/10.1016/j.neucom.2015.12.041.
• 41. Pan H, Yang Y, Zheng J et al. Symplectic interactive support matrix machine and its application in roller bearing condition monitoring. Neurocomputing 2020; 398: 1-10, https://doi.org/10.1016/j.neucom.2020.01.074.
• 42. Ren L, Sun Y, Cui J, Zhang L. Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems 2018; 48: 71-77, https://doi.org/10.1016/j.jmsy.2018.04.008.
• 43. Reuben L C K, Mba D. Diagnostics and prognostics using switching Kalman filters. Structural Health Monitoring 2014; 13(3): 296-306, https://doi.org/10.1177/1475921714522844.
• 44. Samanta B, Al-Balushi K R. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mechanical Systems and Signal Processing 2003; 17(2): 317-328, https://doi.org/10.1006/mssp.2001.1462.
• 45. Si X S, Wang W, Hu C H, Zhou D H. Remaining useful life estimation - a review on the statistical data driven approaches. European Journal of Operational Research 2011; 213(1): 1-14, https://doi.org/10.1016/j.ejor.2010.11.018.
• 46. Singh S, Agarwal T, Yadav G K O M. Predicting the remaining useful life of ball bearing under dynamic loading using supervised learning. Proceedings of the 2019 IEEE, 2019; 1119-1123, https://doi.org/10.1109/IEEM44572.2019.8978649.
• 47. Sobie C, Freitas C, Nicolai M. Simulation-driven machine learning: Bearing fault classification. Mechanical Systems and Signal Processing 2018; 99: 403-419, https://doi.org/10.1016/j.ymssp.2017.06.025.
• 48. Tobon-Mejia D A, Medjaher K, Zerhouni N, Tripot G. A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models. IEEE Transactions on Reliability 2012; 61(2): 491-503, https://doi.org/10.1109/TR.2012.2194177.
• 49. Wang B, Hu X, Li H. Rolling bearing performance degradation condition recognition based on mathematical morphological fractal dimension and fuzzy C-means. Measurement: Journal of the International Measurement Confederation 2017; 109: 1-8, https://doi.org/10.1016/j.measurement.2017.05.033.
• 50. Wang D, Tsui K L. Theoretical investigation of the upper and lower bounds of a generalized dimensionless bearing health indicator. Mechanical Systems and Signal Processing 2018; 98: 890-901, https://doi.org/10.1016/j.ymssp.2017.05.040.
• 51. Wang D, Tsui K L. Statistical modeling of bearing degradation signals. IEEE Transactions on Reliability 2017; 66(4): 1331-1344, https://doi.org/10.1109/TR.2017.2739126.
• 52. Wang Y, Xiang J, Markert R, Liang M. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: a review with applications. Mechanical Systems and Signal Processing 2016; 66-67: 679-698, https://doi.org/10.1016/j.ymssp.2015.04.039.
• 53. Wang Z, Wu Q, Zhang X et al. A generalized degradation model based on Gaussian process. Microelectronics Reliability 2018; 85(4): 207-214, https://doi.org/10.1016/j.microrel.2018.05.001.
• 54. Wen Y, Wu J, Das D, Tseng T L. Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity. Reliability Engineering and System Safety 2018; 176(11): 113-124. https://doi.org/10.1016/j.ress.2018.04.005.
• 55. Wu C, Feng F, Wu S et al. A method for constructing rolling bearing lifetime health indicator based on multi ‑ scale convolutional neural networks. Journal of the Brazilian Society of Mechanical Sciences and Engineering 2019; 41(11): 1-11, https://doi.org/10.1007/s40430-019-2010-6.
• 56. Xu L, Pennacchi P, Chatterton S. A new method for the estimation of bearing health state and remaining useful life based on the moving average cross-correlation of power spectral density. Mechanical Systems and Signal Processing 2020; 139: 106617, https://doi.org/10.1016/j.ymssp.2020.106617.
• 57. Yan M, Wang X, Wang B et al. Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model. ISA Transactions 2019; 98: 471-48, https://doi.org/10.1016/j.isatra.2019.08.058.
• 58. Yaqub M F, Gondal I, Kamruzzaman J. Multi-step support vector regression and optimally parameterized wavelet packet transform for machine residual life prediction. Journal of Vibration and Control 2013; 19(7): 963-974, https://doi.org/10.1177/1077546311435349.
• 59. Yu J. Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models. Mechanical Systems and Signal Processing 2011; 25(7): 2573-2588, https://doi.org/10.1016/j.ymssp.2011.02.006.
• 60. Zhang B, Zhang S, Li W. Bearing performance degradation assessment using long short-term memory recurrent network. Computers in Industry 2019; 106: 14-29, https://doi.org/10.1016/j.compind.2018.12.016.
• 61. Zhang H, Chen X, Du Z, Yan R. Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis. Mechanical Systems and Signal Processing 2016; 80: 349-376, https://doi.org/10.1016/j.ymssp.2016.04.033.
• 62. Zhang Y, Zuo H, Bai F. Classification of fault location and performance degradation of a roller bearing. Measurement 2013; 46(3): 1178-1189, https://doi.org/10.1016/j.measurement.2012.11.025.
• 63. Zhao M, Jin X, Zhang Z, Li B. Fault diagnosis of rolling element bearings via discriminative subspace learning: visualization and classification. Expert Systems with Applications 2014; 41(7): 3391-3401, https://doi.org/10.1016/j.eswa.2013.11.026.
• 64. Zhu J, Chen N, Shen C. A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions. Mechanical Systems and Signal Processing 2020; 139: 106602, https://doi.org/10.1016/j.ymssp.2019.106602.
• 65. Zhu X, Zhang Y, Zhu Y. Bearing performance degradation assessment based on the rough support vector data description. Mechanical Systems and Signal Processing 2013; 34(1-2): 203-217, https://doi.org/10.1016/j.ymssp.2012.08.008.

Uwagi

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