Residual-life distributions from component degradation signals: A Bayesian approach
Reads0
Chats0
TLDR
Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models are developed and used to develop a closed-form residual-life distribution for the monitored device.Abstract:
Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can then be used in decision models. In this work, we develop Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models. We use these degradation models to develop a closed-form residual-life distribution for the monitored device. Finally, we apply these degradation...read more
Citations
More filters
Journal ArticleDOI
Remaining useful life estimation - A review on the statistical data driven approaches
TL;DR: This paper systematically reviews the recent modeling developments for estimating the RUL and focuses on statistical data driven approaches which rely only on available past observed data and statistical models.
Reliability Engineering and System Safety
TL;DR: In this paper, a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions is presented.
Journal ArticleDOI
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.
Journal ArticleDOI
Current status of machine prognostics in condition-based maintenance: a review
Ying Peng,Ming Dong,Ming J. Zuo +2 more
TL;DR: In this article, a review of recent literature that focuses on the machine prognostics has been reviewed, which can be classified into four categories: physical model, knowledge-based model, data-driven model, and combination model.
An introduction to mathematical statistical and its applications / Richard J. Larsen, Morris L. Marx
Richard J. Larsen,Morris L. Marx +1 more
TL;DR: In this article, Monte Carlo techniques are used to estimate the probability of a given set of variables for a particular set of classes of data, such as conditional probability and hypergeometric probability.
References
More filters
Book
Probability: Theory and Examples
TL;DR: In this paper, a comprehensive introduction to probability theory covering laws of large numbers, central limit theorem, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion is presented.
Book
The theory of stochastic processes
David Cox,Hilton D. Miller +1 more
TL;DR: This book should be of interest to undergraduate and postgraduate students of probability theory.
Book
Accelerated Testing: Statistical Models, Test Plans, and Data Analyses
TL;DR: Accelerated Testing: Statistical Models, Test Plans, and Data Analyses, by W. Nelson.
Journal ArticleDOI
Accelerated Testing: Statistical Models, Test Plans, and Data Analyses
TL;DR: In this article, Accelerated Testing: Statistical Models, Test Plans, and Data Analyses Technometrics: Vol 33, No 2, pp 236-238 and this article.
Journal ArticleDOI
Using Degradation Measures to Estimate a Time-to-Failure Distribution
C. Joseph Lu,William O. Meeker +1 more
TL;DR: In this article, the authors developed statistical methods for using degradation measures to estimate a time-to-failure distribution for a broad class of degradation models, using a nonlinear mixed-effects model and developing methods based on Monte Carlo simulation to obtain point estimates and confidence intervals for reliability assessment.
Related Papers (5)
Using Degradation Measures to Estimate a Time-to-Failure Distribution
C. Joseph Lu,William O. Meeker +1 more