scholarly journals Competitive failure analysis of a stochastic degradation system based on performance characteristics fusion

2021 ◽  
Vol 39 (2) ◽  
pp. 439-447
Author(s):  
Qinglai Dong ◽  
Weiwei Wang ◽  
Shubin Si
Keyword(s):  
System Reliability ◽  
Failure Modes ◽  
Reliability Assessment ◽  
Degradation Process ◽  
Assessment Method ◽  
Performance Characteristics ◽  
Reliability Modeling ◽  
Numerical Examples ◽  
Degradation Models ◽  
Bivariate Wiener Process

With the aim of solving the reliability modeling and calculation of multivariate stochastic degradation systems, two stochastic degradation models based on the bivariate Wiener process are proposed, in which two performance characteristics are composited to one variable. Two different failure modes including the defect-based failure and the duration-based failure are considered. The explicit expressions of the system reliability are derived in the cases that the performance characteristics are not composited or the performance characteristics are composited according to the linear combination of the degradation measurements. An algorithm based on the Monte Carlo simulation is proposed to simulate the degradation process, in which the performance characteristics are composited in arbitrary forms, and the correctness of the analytical results is also verified. Finally, some numerical examples are presented to illustrate the present reliability assessment method。

scholarly journals LED Lighting System Reliability Modeling and Inference via Random Effects Gamma Process and Copula Function

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Huibing Hao ◽  
Chun Su ◽  
Chunping Li
Keyword(s):  
Random Effects ◽  
System Reliability ◽  
Light Emitting Diode ◽  
Copula Function ◽  
Performance Characteristics ◽  
Assessment Model ◽  
Gamma Process ◽  
Unknown Parameters ◽  
Reliability Modeling ◽  
Lighting Systems

Light emitting diode (LED) lamp has attracted increasing interest in the field of lighting systems due to its low energy and long lifetime. For different functions (i.e., illumination and color), it may have two or more performance characteristics. When the multiple performance characteristics are dependent, it creates a challenging problem to accurately analyze the system reliability. In this paper, we assume that the system has two performance characteristics, and each performance characteristic is governed by a random effects Gamma process where the random effects can capture the unit to unit differences. The dependency of performance characteristics is described by a Frank copula function. Via the copula function, the reliability assessment model is proposed. Considering the model is so complicated and analytically intractable, the Markov chain Monte Carlo (MCMC) method is used to estimate the unknown parameters. A numerical example about actual LED lamps data is given to demonstrate the usefulness and validity of the proposed model and method.

scholarly journals Reliability Assessment of Space Station Based on Multi-Layer and Multi-Type Risks

2021 ◽  
Vol 11 (21) ◽  
pp. 10258
Author(s):  
Xiaopeng Li ◽  
Fuqiu Li
Keyword(s):  
System Reliability ◽  
Early Stage ◽  
Space Station ◽  
Reliability Assessment ◽  
Assessment Method ◽  
Assessment Process ◽  
Reliability Model ◽  
Using Data ◽  
Propagation Feature ◽  
Sufficient Test

A space station is a typical phased-mission system, and assessing its reliability during its configuration is an important engineering action. Traditional methods usually require extensive data to carry out a layered reliability assessment from components to the system. These methods suffer from lack of sufficient test data, and the assessment process becomes very difficult, especially in the early stage of the configuration. This paper proposes a reliability assessment method for the space station configuration mission, using multi-layer and multi-type risks. Firstly, the risk layer and the risk type for the space station configuration are defined and identified. Then, the key configuration risks are identified comprehensively, considering their occurrence likelihood and consequence severity. High load risks are identified through risk propagation feature analysis. Finally, the configuration reliability model is built and the state probabilities are computed, based on the probabilistic risk propagation assessment (PRPA) method using the assessment probability data. Two issues are addressed in this paper: (1) how to build the configuration reliability model with three layers and four types of risks in the early stage of the configuration; (2) how to quantitatively assess the configuration mission reliability using data from the existing operational database and data describing the propagation features. The proposed method could be a useful tool for the complex aerospace system reliability assessment in the early stage.

Green system reliability assessment method based on life cycle: Resources and economical view

2020 ◽  
Vol 251 ◽  
pp. 119786
Author(s):  
Jun-Gang Zhou ◽  
Ling-Ling Li ◽  
Ming-Lang Tseng ◽  
Guo-Qian Lin
Keyword(s):  
Life Cycle ◽  
System Reliability ◽  
Reliability Assessment ◽  
Assessment Method ◽  
Green System

An Integrated Performance Measure Approach for System Reliability Analysis

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Zequn Wang ◽  
Pingfeng Wang
Keyword(s):  
System Reliability ◽  
Failure Modes ◽  
Reliability Assessment ◽  
Surrogate Models ◽  
Performance Measure ◽  
System Level ◽  
Performance Measure Approach ◽  
Component Failure ◽  
Sample Points ◽  
Integrated Performance

This paper presents a new adaptive sampling approach based on a novel integrated performance measure approach, referred to as “iPMA,” for system reliability assessment with multiple dependent failure events. The developed approach employs Gaussian process (GP) regression to construct surrogate models for each component failure event, thereby enables system reliability estimations directly using Monte Carlo simulation (MCS) based on surrogate models. To adaptively improve the accuracy of the surrogate models for approximating system reliability, an iPM, which envelopes all component level failure events, is developed to identify the most useful sample points iteratively. The developed iPM possesses three important properties. First, it represents exact system level joint failure events. Second, the iPM is mathematically a smooth function “almost everywhere.” Third, weights used to reflect the importance of multiple component failure modes can be adaptively learned in the iPM. With the weights updating process, priorities can be adaptively placed on critical failure events during the updating process of surrogate models. Based on the developed iPM with these three properties, the maximum confidence enhancement (MCE) based sequential sampling rule can be adopted to identify the most useful sample points and improve the accuracy of surrogate models iteratively for system reliability approximation. Two case studies are used to demonstrate the effectiveness of system reliability assessment using the developed iPMA methodology.

scholarly journals System Reliability Assessment with Imprecise Probabilities

2019 ◽  
Vol 9 (24) ◽  
pp. 5422 ◽  
Author(s):  
Guodong Yang ◽  
Xianzhen Huang ◽  
Yuxiong Li ◽  
Pengfei Ding
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Reliability Assessment ◽  
Statistical Characteristics ◽  
Imprecise Probabilities ◽  
Limited Sample ◽  
Numerical Examples ◽  
Practical Engineering ◽  
Life Distributions ◽  
High Level

The exact statistical characteristics of some components may be unavailable because of the limited sample information in practical engineering. One challenge that system reliability analysis faces is dealing with limited sample sizes, which introduces the potential for a high level of uncertainty in the analysis results. In this paper, we propose a procedure for the reliability analysis of complex systems with a limited number of samples. Bayesian inference is used to estimate the parameter intervals of the life distributions of the components with a limited number of samples. Then, probability boxes (p-box) are constructed from the parameter intervals to represent the life distributions of the components with a limited number of samples. In addition, the theory of survival signature is applied to calculate the reliability of the system with a mixture of precise and imprecise knowledge of the life distributions of the components. Finally, two numerical examples are given to illustrate the validity of the methods.

Reliability Demonstration Method for Competing Failure System

2020 ◽  
Vol 27 (04) ◽  
pp. 2050015
Author(s):  
Teng Fei ◽  
Hao-Wei Wang
Keyword(s):  
Failure Modes ◽  
Reliability Assessment ◽  
Competing Risk ◽  
Failure Model ◽  
Reliability Modeling ◽  
Space Model ◽  
Degradation Data ◽  
System A ◽  
Degradation Failure ◽  
Degradation Degree

In order to improve the accuracy of reliability assessment for the system with multiple failure modes, a reliability modeling method based on the s-dependent competing risk of degradation failures and traumatic failures was proposed. A condition space model was applied to evaluate the degradation degree of system by fusing multivariate degradation data, and then Gamma process was utilized to establish the degradation failure model of system. A conditional Weibull distribution was used to establish the traumatic failure model of system. The reliability model based on dependent competing risk was established by assuming that the probability of traumatic failure depends on the degradation degree of system. An illustrative example was provided to validate the effectiveness of the proposed method.

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