TY - JOUR
T1 - Remaining Useful Life Model and Assessment of Mechanical Products
T2 - A Brief Review and a Note on the State Space Model Method
AU - Hu, Yawei
AU - Liu, Shujie
AU - Lu, Huitian
AU - Zhang, Hongchao
N1 - Funding Information:
HL and HZ was in charge of the whole trial; YH and SL wrote the manuscript; All authors read and approved the final manuscript. Yawei Hu, born in 1989, is currently a PhD candidate at Dalian University of Technology, China. Shujie Liu, born in 1977, is currently an associate professor at Dalian University of Technology, China. She received his PhD degree from the University of Tokyo, Japan , in 2007. Her research interests include remaining useful life assessment, precision engineering and sustainable engineering. Huitian Lu, born in 1948, is currently a professor at South Dakota State University, USA. He received his PhD degree from Texas Tech University, USA , in 1998. His research interests include reliability estimation, life prediction and dynamic modelling. Hongchao Zhang, born in 1953, is currently a Thousands of People Plan Distinguished Professor at Dalian University of Technology, China , is also professor of the Department of Industrial Engineering, Texas Tech University, USA. He received his PhD degree from Technical University of Denmark, Denmark , in 1989. His research interests include green manufacturing, remanufacturing and LCA. The authors declare no that they have no competing interests. Supported by Fundamental Research Funds for the Central Universities of China (Grant No. DUT17GF214). Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Funding Information:
Supported by Fundamental Research Funds for the Central Universities of China (Grant No. DUT17GF214).
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - The remaining useful life (RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety (safety awareness and safety improvement). These studies incorporated many different models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and difficulty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model (SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation (sequential Monte Carlo). Being effective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM (condition based maintenance), PHM (prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.
AB - The remaining useful life (RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety (safety awareness and safety improvement). These studies incorporated many different models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and difficulty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model (SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation (sequential Monte Carlo). Being effective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM (condition based maintenance), PHM (prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.
KW - Bayesian estimation
KW - Online assessment
KW - Particle filter
KW - Remaining useful life
KW - Remanufacturing
KW - State space model
UR - http://www.scopus.com/inward/record.url?scp=85069449445&partnerID=8YFLogxK
U2 - 10.1186/s10033-019-0317-y
DO - 10.1186/s10033-019-0317-y
M3 - Review article
AN - SCOPUS:85069449445
VL - 32
JO - Chinese Journal of Mechanical Engineering (English Edition)
JF - Chinese Journal of Mechanical Engineering (English Edition)
SN - 1000-9345
IS - 1
M1 - 15
ER -