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Similarity-based failure threshold determination for system residual life prediction
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School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District Beijing100081, China
Publication date: 2020-09-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(3):520-529
An accurate determination of the system failure threshold is an essential requirement in achieving an appropriate system residual life prediction and a reasonable planned maintenance strategy optimization afterward for degradation systems. This paper proposes a failure threshold determination method based on quantitative measurement of the similarity between the operating system and the historical systems. The similarity is formulated by a weighted average function and then calculated by a convex quadratic formulation to minimizing the variance between the operating system and the historical systems. With an accurate determination of the system failure threshold in real-time, a better prediction of the residual life for the operating system is achieved. Finally, a real case study for several power-shift steering transmission systems monitored using oil spectral analysis is adopted to illustrate and numerically compare the improved performance of the proposed method.
Alaswad S, Xiang Y. A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliability Engineering & System Safety 2017; 157: 54-63,
Bian L, Gebraeel N, Kharoufeh J P. Degradation modeling for real-time estimation of residual lifetimes in dynamic environments. IIE Transactions 2015; 47(5): 471-486,
Caballé N C, Castro I T, Pérez C J, Lanza-Gutiérrez J. M. A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes. Reliability Engineering & System Safety 2015; 134: 98-109,
Chehade A, Bonk S, Liu K. Sensory-based failure threshold estimation for remaining useful life prediction. IEEE Transactions on Reliability2017; 66(3): 939-949,
Chinnam R B. On-line reliability estimation for individual components using statistical degradation signal models. Quality and Reliability Engineering International 2002; 18(1): 53-73,
Du Y, Wu T, Makis V. Parameter estimation and remaining useful life prediction of lubricating oil with HMM. Wear 2017; 376: 1227-1233,
Elwany A H, Gebraeel N Z. Sensor-driven prognostic models for equipment replacement and spare parts inventory. IIE Transactions 2008; 40(7): 629-639,
Giraitis L, Kapetanios G, Yates T. Inference on multivariate heteroscedastic time varying random coefficient models. Journal of Time Series Analysis 2018; 39(2): 129-149.
Keizer M C O, Flapper S D P, Teunter R H. Condition-based maintenance policies for systems with multiple dependent components: A review. European Journal of Operational Research 2017; 261(2): 405-420,
Kim M J, Jiang R, Makis V, Lee C G. Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure. European Journal of Operational Research 2011; 214(2): 331-339,
Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI 1995; 14(2): 1137-1145.
Kozłowski E, Mazurkiewicz D, Żabiński T, Prucnal S, Sęp J. Assessment model of cutting tool condition for real-time supervision system. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21 (4): 679-685,
Lee J, Wu F, Zhao W, Ghaffari M, Liao L, Siegel D. Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications. Mechanical systems and signal processing 2014; 42(1-2): 314-334,
Lei Y, Li N, Guo L, Li N, Yan T, Lin J. Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing 2018;104: 799-834,
Li X, Makis V, Zuo H, Cai J. Optimal Bayesian control policy for gear shaft fault detection using hidden semi-Markov model. Computers & Industrial Engineering 2018; 119: 21-35,
Liao L. Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Transactions on Industrial Electronics 2013; 61(5): 2464-2472,
Liu, K., Gebraeel, N. Z., Shi, J. A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering 2013; 10(3): 652-664.
Liu K, Huang S. Integration of data fusion methodology and degradation modeling process to improve prognostics. IEEE Transactions on Automation Science and Engineering 2014;13(1): 344-354,
Liu, X., Li, J., Al-Khalifa, K. N., Hamouda, A. S., Coit, D. W., Elsayed, E. A. Condition-based maintenance for continuously monitored degrading systems with multiple failure modes. IIE transactions 2013; 45(4): 422-435.
Liu Y, Ma B, Zheng C S, Xie S Y. Failure prediction of power-shift steering transmission based on oil spectral analysis with Wiener process. Spectroscopy and Spectral Analysis 2015; 35(9): 2620-2624.
Okoh C, Roy R, Mehnen J, Redding L. Overview of remaining useful life prediction techniques in through-life engineering services. Procedia CIRP 2014; 16: 158-163,
Tang S J, Yu C Q, Feng Y B, Xie J, Gao Q H, Si X S. Remaining useful life estimation based on Wiener degradation processes with random failure threshold. Journal of Central South University 2016; 23(9): 2230-2241,
Tian Z, Wong L, Safaei N. A neural network approach for remaining useful life prediction utilizing both failure and suspension histories. Mechanical Systems and Signal Processing 2010; 24(5): 1542-1555,
Vališ D, Žák L, Pokora O, Lánský P. Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance. Reliability Engineering & System Safety 2016; 145: 231-242,
Wakiru J M, Pintelon L, Muchiri P N, Chemweno P K. A review on lubricant condition monitoring information analysis for maintenance decision support. Mechanical Systems and Signal Processing 2019; 118: 108-132,
Wang D, Tsui K L. Brownian motion with adaptive drift for remaining useful life prediction: Revisited. Mechanical Systems and Signal Processing 2018; 99: 691-701,
Wang J, Makis V, Zhao X. Optimal condition-based and age-based opportunistic maintenance policy for a two-unit series system. Computers & Industrial Engineering2019; 134: 1-10,
Xiao N, Huang H Z, Li Y, He L, Jin T. Multiple failure modes analysis and weighted risk priority number evaluation in FMEA. Engineering Failure Analysis 2011; 18(4): 1162-1170,
Yan S, Ma B, Zheng C. Degradation index construction methodology for mechanical transmission based on fusion of multispectral oil data. Industrial Lubrication and Tribology 2019; 71(2): 278-283,
Yan S, Ma B, Zheng C. Health index extracting methodology for degradation modelling and prognosis of mechanical transmissions. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(1): 137-144,
Yan S, Ma B, Wang X, Zheng C. Maintenance policy for oil-lubricated systems with oil analysis data. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2020; 22(3): 455-464,
Yan S F, Ma B, Zheng C S. Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra. Advances in Mechanical Engineering 2018; 10(6): 1687814018784201,
Yan S F, Ma B, Zheng C S, Chen M. Weighted evidential fusion method for fault diagnosis of mechanical transmission based on oil analysis data. International Journal of Automotive Technology 2019; 20(5): 989-996,
Yan S F, Ma B, Zheng C S, Zhu L A, Chen J W, Li H Z. Remaining useful life prediction of power-shift steering transmission based on uncertain oil spectral data. Spectroscopy and Spectral Analysis 2019; 39(2): 553-558.
Ye Z S, Xie M. Stochastic modelling and analysis of degradation for highly reliable products. Applied Stochastic Models in Business and Industry 2015; 31(1) 16-32,
Zhai Q, Ye Z S. Degradation in common dynamic environments. Technometrics 2018; 60(4): 461-471,
Zhang C, Lim P, Qin A K, Tan K C. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE transactions on neural networks and learning systems 2016; 28(10): 2306-2318,
Zhang Z, Si X, Hu C, Lei Y. Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods. European Journal of Operational Research2018; 271(3): 775-796,
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