Search for Author, Title, Keyword
Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model
More details
Hide details
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University; Xi’an 710049, China
Publication date: 2021-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(1):154-164
  • Reducing the prognostic uncertainty by making full use of operating data.
  • Extracting degradation characteristics from longterm running data.
  • Using degradation characteristics as input variables to obtain the survival function.
  • Targeted modeling for the last degradation state.
Uncertainty is a key problem in remaining useful life (RUL) prediction, and measures to reduce uncertainty are necessary to make RUL prediction truly practical. In this paper, a right-time prediction method is proposed to reduce the prognostics uncertainty of mechanical systems under unobservable degradation. Correspondingly, the whole RUL prediction process is divided into three parts, including offline modelling, online state estimating and online life predicting. In the offline modelling part, hidden Markov model (HMM) and proportional hazard model (PHM) are built to map the whole degradation path. During operation, the degradation state of the object is estimated in real time. Once the last degradation state reached, the degradation characteristics are extracted, and the survival function is obtained with the fitted PHM. The proposed method is demonstrated on an engine dataset and shows higher accuracy than traditional method. By fusing the extracted degradation characteristics, the obtained survival function can be basis for optimal maintenance with lower uncertainty.
Bai J M, Zhao G S, Rong H J. Novel direct remaining useful life estimation of aero-engines with randomly assigned hidden nodes. Neural Computing and Applications 2020; 32(18): 14347-14358,
Baraldi P, Mangili F, Zio E. A belief function theory based approach to combining different representation of uncertainty in prognostics. Information Sciences 2015; 303: 134-149,
Cai B P, Shao X Y, Liu Y H, Kong X D, Wang H F, Xu H Q, Ge W F. Remaining useful life estimation of structure systems under the influence of multiple causes: subsea pipelines as a case study. IEEE Transactions on Industrial Electronics 2020; 67(7): 5737-5747,
Chen Z, Li Y, Xia T, Pan E. Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy. Reliability Engineering & System Safety 2019; 184: 123-136,
Cox D R. Regression models and life‐tables. Journal of the Royal Statistical Society: Series B (Methodological) 1972; 34(2): 187-220,
Deng Y, Bucchianico A D, Pechenizkiy M. Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate Wiener propagation model. Reliability Engineering & System Safety 2020; 196: 1-10,
Djeziri M A, Benmoussa S, Sanchez R. Hybrid method for remaining useful life prediction in wind turbine systems. Renewable Energy 2018; 116: 173-187,
Du Y, Wu T, Zhou S, Makis V. Remaining useful life prediction of lubricating oil with dynamic principal component analysis and proportional hazards model. Proceedings of the Institution of Mechanical Engineers Part J-Journal of Engineering Tribology 2020; 234(6): 964-971,
Engel S J, Gilmartin B J, Bongort K, Hess A. Prognostics, the real issues involved with predicting life remaining. IEEE Aerospace Conference. 2000: 457-469.
Equeter L, Ducobu F, Rivière-Lorphèvre E, Serra R, Dehombreux P. An analytic approach to the Cox proportional hazards model for estimating the lifespan of cutting tools. Journal of Manufacturing and Materials Processing 2020; 4(27): 1-13,
Fink O, Wang Q, Svensén M, Dersin P, Ducoffe M. Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications. Engineering Applications of Artificial Intelligence 2020; 92: 1-15,
Hu J W, Chen P. Predictive maintenance of systems subject to hard failure based on proportional hazards model. Reliability Engineering & System Safety 2020; 196: 1-9,
Hu Y, Li H, Shi P, Chai Z, Wang K, Xie X, Chen Z. A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process. Renewable Energy 2018; 127: 452-460,
Jacyna M, Semenov I. Models of vehicle service system supply under information uncertainty. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(4): 694-704,
Lee D, Choi D. Analysis of the reliability of a starter-generator using a dynamic Bayesian network. Reliability Engineering & System Safety 2020; 195: 1-11,
Li H, Wang W, Li Z W, Dong L Y, Li Q Z. A novel approach for predicting tool remaining useful life using limited data. Mechanical Systems and Signal Processing 2020; 143: 1-22,
Li X P, Huang H Z, Li F Q, Ren L M. Remaining useful life prediction model of the space station. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(3): 501-510,
Liang Z, Parlikad A K. Predictive group maintenance for multi-system multi-component networks. Reliability Engineering & System Safety 2020; 195: 1-18,
Lin L, Sun Z, Xu X, Zhang K. Multi-zone proportional hazard model for a multi-stage degradation process. 8th International Manufacturing Science and Engineering Conference. 2013: 1-8,
Liu D, Wang S P, Tomovic M M. Degradation modeling method for rotary lip seal based on failure mechanism analysis and stochastic process. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(3): 381-390,
Ma B, Yan S, Wang X, Chen J, Zheng C. Similarity-based failure threshold determination for system residual life prediction. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(3): 520-529,
Maior C B S, Moura M D C, Lins I D. Particle swarm-optimized support vector machines and pre-processing techniques for remaining useful life estimation of bearings. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(4): 610-619,
Man J, Zhou Q. Prediction of hard failures with stochastic degradation signals using Wiener process and proportional hazards model. Computers & Industrial Engineering 2018; 125: 480-489,
Ossai C I. Prognostics health estimation of lithium-ion batteries in charge-decay estimation uncertainties - A comparative analysis. International Journal of Prognostics and Health Management 2018; 9(2): 1-8.
Qiu G, Gu Y, Chen J. Selective health indicator for bearings ensemble remaining useful life prediction with genetic algorithm and Weibull proportional hazards model. Measurement 2020; 150: 1-14,
Rabiner L R. A tutorial on hidden Markov-models and selected applications in speech recognition. Proceedings of the IEEE 1989; 77(2): 257-286,
Ramezani S, Moini A, Riahi M. Prognostics and health management in machinery: A review of methodologies for RUL prediction and roadmap. International Journal of Industrial Engineering and Management Science 2019; 6(1): 38-61.
Razavi-Far R, Chakrabarti S, Saif M, Zio E. An integrated imputation-prediction scheme for prognostics of battery data with missing observations. Expert Systems with Applications 2019; 115: 709-723,
Sankararaman, Shankar. Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems & Signal Processing 2015; 52: 228-247,
Sankararaman S, Goebel K. Uncertainty in prognostics and systems health management. International Journal of Prognostics & Health Management 2015; 6: 1-11.
Saxena A, Goebel K, Simon D, Eklund N. Damage propagation modeling for aircraft engine run-to-failure simulation. 2008 international conference on prognostics and health management. 2008: 1-9,
Sikorska J, Hodkiewicz M, Ma L. Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing 2011; 25(5): 1803-1836,
Sun J, Zuo H, Wang W, Pecht M G. Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model. Mechanical Systems and Signal Processing 2014; 45(2): 396-407,
Tayade A, Patil S, Phalle V, Kazi F, Powar S. Remaining useful life (RUL) prediction of bearing by using regression model and principal component analysis (PCA) technique. Vibroengineering Procedia 2019; 23: 30-36,
Tran V T, Hong T P, Yang B S, Tan T N. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine. Mechanical Systems & Signal Processing 2012; 32: 320-330,
Wang H Y, Song W Q, Zio E, Kudreyko A, Zhang Y J. Remaining useful life prediction for Lithium-ion batteries using fractional Brownian motion and Fruit-fly Optimization Algorithm. Measurement 2020; 161: 1-9,
Wang T. Trajectory similarity based prediction for remaining useful life estimation. University of Cincinnati; 2010.
Xiuli, Wang, Bin, Jiang, Ningyun, Lu. Adaptive relevant vector machine based RUL prediction under uncertain conditions. ISA Transactions 2019; 87: 217-224,
Yang C, Zhang Y, Xu X, Li W. Molecular subtypes based on DNA methylation predict prognosis in colon adenocarcinoma patients. Aging 2019; 11(24): 11880-11892,
You M Y, Li L, Meng G, Ni J. Two-zone proportional hazard model for equipment remaining useful life prediction. Journal of Manufacturing Science and Engineering-Transactions of the Asme 2010; 132(4): 1-6,
Yu W N, Kim I Y, Mechefske C. An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme. Reliability Engineering & System Safety 2020; 199: 1-12,
Zhang W, Zhang Y. Integrated survival analysis of mRNA and microRNA signature of patients with breast cancer based on Cox model. Journal of Computational Biology: a Journal of Computational Molecular Cell Biology 2020,
Zhou Q, Son J, Zhou S, Mao X, Salman M A. Remaining useful life prediction of individual units subject to hard failure. IIE Transactions 2014; 46(10): 1017-1030,
Journals System - logo
Scroll to top