scholarly journals Reliability Modeling for Systems with Multiple Degradation Processes Using Inverse Gaussian Process and Copulas

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenyu Liu ◽  
Xiaobing Ma ◽  
Jun Yang ◽  
Yu Zhao
Keyword(s):  
Degradation Process ◽  
Reliability Estimation ◽  
Scale Transformation ◽  
Distribution Model ◽  
Inverse Gaussian ◽  
Random Drift ◽  
Reliability Modeling ◽  
Degradation Processes ◽  
Inverse Gaussian Process ◽  
Comparison Results

We develop a reliability model for systems with s-dependent degradation processes using copulas. The proposed model accommodates assumptions of s-dependence among degradation processes and allows for different marginal distributions. This flexibility makes the model more attractive compared with the multivariate distribution model, which lay on the limitation of the homogeneous marginal distribution and can only describe linear correlation. Marginal degradation process is modeled by the inverse Gaussian (IG) process with time scale transformation. Furthermore, we incorporate random drift to account for the possible heterogeneity in population. This paper also develops the statistical inference method using EM algorithm with two-stage procedure. The comparison results of the reliability estimation under both s-dependent and s-independent assumptions are illustrated in the illustrative example to demonstrate the applicability of the proposed method.

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

scholarly journals A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 389
Author(s):  
Jinfu Liu ◽  
Zhenhua Long ◽  
Mingliang Bai ◽  
Linhai Zhu ◽  
Daren Yu
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Detection Methods ◽  
Distribution Model ◽  
Outlet Temperature ◽  
Combustion System ◽  
Comparison Results ◽  
Gas Turbine Combustion

As one of the core components of gas turbines, the combustion system operates in a high-temperature and high-pressure adverse environment, which makes it extremely prone to faults and catastrophic accidents. Therefore, it is necessary to monitor the combustion system to detect in a timely way whether its performance has deteriorated, to improve the safety and economy of gas turbine operation. However, the combustor outlet temperature is so high that conventional sensors cannot work in such a harsh environment for a long time. In practical application, temperature thermocouples distributed at the turbine outlet are used to monitor the exhaust gas temperature (EGT) to indirectly monitor the performance of the combustion system, but, the EGT is not only affected by faults but also influenced by many interference factors, such as ambient conditions, operating conditions, rotation and mixing of uneven hot gas, performance degradation of compressor, etc., which will reduce the sensitivity and reliability of fault detection. For this reason, many scholars have devoted themselves to the research of combustion system fault detection and proposed many excellent methods. However, few studies have compared these methods. This paper will introduce the main methods of combustion system fault detection and select current mainstream methods for analysis. And a circumferential temperature distribution model of gas turbine is established to simulate the EGT profile when a fault is coupled with interference factors, then use the simulation data to compare the detection results of selected methods. Besides, the comparison results are verified by the actual operation data of a gas turbine. Finally, through comparative research and mechanism analysis, the study points out a more suitable method for gas turbine combustion system fault detection and proposes possible development directions.

Distribution of Acidity and Alkalinity on Degraded Manuscripts Containing Iron Gall Ink

2015 ◽  
Vol 36 (3) ◽  
Author(s):  
Eva Marín ◽  
Maria Carme Sistach ◽  
Jessica Jiménez ◽  
Miguel Clemente ◽  
Guillem Garcia ◽  
...  
Keyword(s):  
Good Condition ◽  
Degradation Process ◽  
Degradation Processes ◽  
Alkaline Reserve ◽  
Hydrolysis Of Cellulose ◽  
Relevant Variables ◽  
Long Time ◽  
Iron Gall ◽  
The Stability

AbstractLong-time preservation of manuscripts depends on the stability of their support. One of the most important degradation processes of paper manuscripts containing iron gall ink is the acid hydrolysis of cellulose. The heterogeneity of the distribution of their constituent materials, together with the defined position of ink as a source of degradation agents, makes it difficult to obtain reliable and detailed information about degradation processes. The aim of this study is to contribute to the knowledge of the acid degradation process by looking at the distribution of relevant variables (pH, acidity and alkaline reserve) on real untreated iron gall ink containing manuscripts at different degradation stages. The study discusses the well-known differences between surface and cold extraction pH determination. It corroborates the relationship between pH, acidity and alkalinity and degradation stages, pointing out that acidity values for some manuscripts in apparent good condition are not far from those obtained for degraded manuscripts. The results indicate that in some partially degraded manuscripts, the coexistence of acid areas and areas with an alkaline reserve which do not participate in the neutralization process is possible. The role of water as a solvent for this equilibrium has also been evaluated.

Inverse Gaussian process models for degradation analysis: A Bayesian perspective

2014 ◽  
Vol 130 ◽  
pp. 175-189 ◽  
Author(s):  
Weiwen Peng ◽  
Yan-Feng Li ◽  
Yuan-Jian Yang ◽  
Hong-Zhong Huang ◽  
Ming J. Zuo
Keyword(s):  
Gaussian Process ◽  
Process Models ◽  
Inverse Gaussian ◽  
Inverse Gaussian Process ◽  
Degradation Analysis ◽  
Gaussian Process Models

scholarly journals A Copula Entropy Approach to Dependence Measurement for Multiple Degradation Processes

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 724 ◽  
Author(s):  
Fuqiang Sun ◽  
Wendi Zhang ◽  
Ning Wang ◽  
Wei Zhang
Keyword(s):  
Information Entropy ◽  
Complex Structure ◽  
Copula Function ◽  
Dependence Structure ◽  
Entropy Theory ◽  
Multiple Features ◽  
Reliability Modeling ◽  
Degradation Processes ◽  
Degradation Analysis ◽  
Complex Dependence

Degradation analysis has been widely used in reliability modeling problems of complex systems. A system with complex structure and various functions may have multiple degradation features, and any of them may be a cause of product failure. Typically, these features are not independent of each other, and the dependence of multiple degradation processes in a system cannot be ignored. Therefore, the premise of multivariate degradation modeling is to capture and measure the dependence among multiple features. To address this problem, this paper adopts copula entropy, which is a combination of the copula function and information entropy theory, to measure the dependence among different degradation processes. The copula function was employed to identify the complex dependence structure of performance features, and information entropy theory was used to quantify the degree of dependence. An engineering case was utilized to illustrate the effectiveness of the proposed method. The results show that this method is valid for the dependence measurement of multiple degradation processes.

Treating Epistemic Uncertainty Using Bootstrapping Selection of Input Distribution Model for Confidence-Based Reliability Assessment

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Min-Yeong Moon ◽  
K. K. Choi ◽  
Nicholas Gaul ◽  
David Lamb
Keyword(s):  
Physical System ◽  
Epistemic Uncertainty ◽  
Bootstrap Method ◽  
Reliability Assessment ◽  
Assessment Method ◽  
Reliability Estimation ◽  
Distribution Model ◽  
Hierarchical Bayesian ◽  
Distribution Models ◽  
Input Distribution

Accurately predicting the reliability of a physical system under aleatory uncertainty requires a very large number of physical output testing. Alternatively, a simulation-based method can be used, but it would involve epistemic uncertainties due to imperfections in input distribution models, simulation models, and surrogate models, as well as a limited number of output testing due to cost. Thus, the estimated output distributions and their corresponding reliabilities would become uncertain. One way to treat epistemic uncertainty is to use a hierarchical Bayesian approach; however, this could result in an overly conservative reliability by integrating possible candidates of input distribution. In this paper, a new confidence-based reliability assessment method that reduces unnecessary conservativeness is developed. The epistemic uncertainty induced by a limited number of input data is treated by approximating an input distribution model using a bootstrap method. Two engineering examples and one mathematical example are used to demonstrate that the proposed method (1) provides less conservative reliability than the hierarchical Bayesian analysis, yet (2) predicts the reliability of a physical system that satisfies the user-specified target confidence level, and (3) shows convergence behavior of reliability estimation as numbers of input and output test data increase.

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