Advances in Sequential Monte Carlo methods for joint state and parameter estimation applied to prognostics
Abstract
The primary objective of this study is to develop a mathematical framework for failure prognostics and uncertainty management based on a state-space degradation model, where unknown parameters are present in the model. Joint state and parameter estimation is carried out by the Sequential Monte Carlo methods. Then, a degradation prediction with uncertainty limits is made. Sequential Monte Carlo methods for joint state and parameter estimation are reviewed, and the effectiveness of the techniques for the prognostics task is assessed. A performance comparison of these algorithms is made based on a numerical study. The results presented in this study show that the state-of-art SMC techniques can provide satisfactory results of joint state and parameter estimation for the prognostics task. The Particle Markov Chain Monte Carlo method provides relatively more accurate estimation results compared with other SMC methods while demanding more computing resources.
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Accurate prognostics requires reliable measurement of the states of the system, which can be compromised by sensor degradation and the resulting measurement uncertainty.
“Accurate prognostics requires reliable measurement on the states of the system, which can be compromised by sensor degradation and the resulting measurement uncertainty”
From Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making: (2025) · cites this paper as Sun, Zuo and Pecht, 2011
Cited by
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Jin, G., Matthews, D. E., & Zhou, Z. (2013) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2012.12.011 · Cited by 163 · see citing works
Thai, T.-T. N., Do, P., Iung, B., & Gardoni, P. (2025) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2025.v16i4.4306 · Cited by 5 · see citing works
- Authors
- Sun, J., Zuo, H., & Pecht, M. G.
- Affiliations
- Nanjing University of Aeronautics and Astronautics (CN)
- University of Maryland, College Park (US)
- Venue
- 2011 Prognostics and System Health Managment Confernece
- Publisher
- IEEE
- Published
- 2011-05
- Pages
- 1-7
- OpenAlex
- W2095878076
- Cited by
- 7
- References
- 0
- Citations by year
- 2018: 1 · 2017: 1 · 2015: 1 · 2014: 2 · 2012: 2
- FWCI
- 4.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Prognostics and System Health Management Conference (publishedVersion)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- failure prognostics, state and parameter estimation, sequential Monte Carlo, particle Markov chain Monte Carlo, uncertainty management, degradation prediction, unknown parameters
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