TY - JOUR
T1 - Sequential Monte Carlo Method Toward Online RUL Assessment with Applications
AU - Hu, Ya Wei
AU - Zhang, Hong Chao
AU - Liu, Shu Jie
AU - Lu, Hui Tian
N1 - Funding Information:
Y-WH designed the study, performed the assays and prepared the manuscript, contributed in its application part. H-CZ conducted the optimization and assay validation studies. H-CZ and S-JL participated in discussing the results, and revised the manuscript. H-TL did the final sequence alignment in the manuscript and drafted the manuscript. All authors read and approved the final manuscript. Ya-Wei Hu, born in 1989, is currently a PhD candidate at Department of Mechanical Engineering, Dalian University of Technology, China. Her research interests include reliability theory, stochastic filtering theory, remaining useful life predication. Hong-Chao Zhang, an Interim Chair & E.L Derr Endowed Professor at Texas Tech University, USA. He is now working in Texas Tech University and Dalian University of Technology, China.?Dr. Hong-Chao Zhang?s research groups have been redesigning end-of-life (EOL) strategies for many products such as heavy-duty equipment remanufacturing, and developing new materials technologies to make sustainable products. Zhang?s groups developed new processes for delaminating, recycling of printed circuit boards using a supercritical carbon dioxide process. The groups carried out microstructural transformation of materials such as shape memory polymer nanocomposites for active disassembly (AD) applications. Zhang?s groups have also extended literature on product innovation and sustainable manufacturing by developing sustainability index and metric for 3D assessment of product?s sustainability. Shu-Jie Liu, born in 1977, is currently a lecturer at Dalian University of Technology, China. She received her PhD degree from the University of Tokyo, Japan , in 2007. Her research interests include remaining useful life assessment, precision engineering and sustainable engineering. Hui-Tian Lu is a professor at Department of Engineering Technology and Management, College of Engineering, South Dakota State University, USA. Supported by Basic Research and Development Plan of China (973 Program, Grant Nos. 2011CB013401, 2011CB013402), and Special Fundamental Research Funds for Central Universities of China (Grant No. DUT14QY21). The authors declare that they have no competing interests. Not applicable. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Funding Information:
Supported by Basic Research and Development Plan of China (973 Program, Grant Nos. 2011CB013401, 2011CB013402), and Special Fundamental Research Funds for Central Universities of China (Grant No. DUT14QY21).
Publisher Copyright:
© 2018, The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - Online assessment of remaining useful life (RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However, there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device. In this paper, state space model (SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo (MCMC) to Sequential Monte Carlo (SMC) algorithm is presented in order to derive the optimal Bayesian estimation. In the context of nonlinear & non-Gaussian dynamic systems, SMC (also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.
AB - Online assessment of remaining useful life (RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However, there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device. In this paper, state space model (SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo (MCMC) to Sequential Monte Carlo (SMC) algorithm is presented in order to derive the optimal Bayesian estimation. In the context of nonlinear & non-Gaussian dynamic systems, SMC (also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.
KW - Bayesian estimation
KW - Milling cutter lifetime
KW - Particle filter
KW - Remaining useful life
KW - Sequential Monte Carlo method
KW - State-space model
KW - Stochastic processes
UR - http://www.scopus.com/inward/record.url?scp=85054068494&partnerID=8YFLogxK
U2 - 10.1186/s10033-018-0205-x
DO - 10.1186/s10033-018-0205-x
M3 - Article
AN - SCOPUS:85054068494
VL - 31
JO - Chinese Journal of Mechanical Engineering (English Edition)
JF - Chinese Journal of Mechanical Engineering (English Edition)
SN - 1000-9345
IS - 1
M1 - 5
ER -