A Framework for Underground Gas Storage System Reliability Assessment Considering Functional Failure of Repairable Components

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Lei He ◽  
Kai Wen ◽  
Jing Gong ◽  
Changchun Wu
Keyword(s):  
System Reliability ◽  
Reliability Assessment ◽  
Single Layer ◽  
Operation Time ◽  
Gas Storage ◽  
System Level ◽  
Ann Model ◽  
Underground Gas Storage ◽  
Operation Conditions ◽  
Repairable Components

Abstract As one of the most important means of nature gas peak shaving and energy strategic reserving, the reliability assessment of underground gas storage (UGS) system is necessary. Although many methods have been proposed for system reliability assessment, the functional heterogeneity of components and the influence of hydrothermal parameters on system reliability are neglected. To overcome these problems, we propose and apply a framework to assess UGS system reliability. Combining two-layer Monte Carlo simulation (MCS) technique with hydrothermal calculation, the framework integrates dynamic functional reliability of components into system reliability evaluation. To reflect the state transition process of repairable components and their impact on system reliability, the Markov model is introduced at system level. In order to improve the calculation speed, artificial neural network (ANN) model based on off-line MCS is established to replace the on-line MCS at components level. The proposed framework is applied to the reliability assessment and operation optimization of an UGS under different operation conditions. Compared with the traditional single-layer MCS method, the proposed method can not only reflect the variation of UGS reliability with hydrothermal parameters and operation time, but also can improve evaluation efficiency significantly.

A Framework for Underground Gas Storage System Reliability Assessment Considering Functional Failure of Repairable Components

2019 ◽  
Author(s):  
Lei He ◽  
Kai Wen ◽  
Jing Gong ◽  
Changchun Wu
Keyword(s):  
System Reliability ◽  
Storage System ◽  
Reliability Assessment ◽  
Single Layer ◽  
Operation Time ◽  
Gas Storage ◽  
System Level ◽  
Underground Gas Storage ◽  
Operation Conditions ◽  
Repairable Components

Abstract As one of the most important means of nature gas peak shaving and energy strategic reserving, the reliability assessment of underground gas storage (UGS) system is necessary. Although many methods have been proposed for system reliability assessment, the functional heterogeneity of components and the influence of hydrothermal parameters on system reliability are neglected. To overcome these problems, we propose and apply a framework to assess UGS system reliability. Combining two-layer Monte Carlo simulation (MCS) technique with hydrothermal calculation, the framework integrates dynamic functional reliability of components into system reliability evaluation. To reflect the state transition process of repairable components and their impact on system reliability, the Markov model is introduced at system level. In order to improve the calculation speed, artificial neural network model based on off-line MCS is established to replace the on-line MCS at components level. The proposed framework is applied to the reliability assessment and operation optimization of an UGS under different operation conditions. Compared with the traditional single-layer MCS method, the proposed method can not only reflect the variation of UGS reliability with hydrothermal parameters and operation time, but also can improve evaluation efficiency significantly.

A Sequential Accelerated Life Testing Framework for System Reliability Assessment With Untestable Components

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Zhen Hu ◽  
Zissimos P. Mourelatos
Keyword(s):  
System Reliability ◽  
Reliability Assessment ◽  
Reliability Evaluation ◽  
Accelerated Life Testing ◽  
System Level ◽  
Life Testing ◽  
Component Level ◽  
Accelerated Life ◽  
Design Cycle ◽  
Framework System

Testing of components at higher-than-nominal stress level provides an effective way of reducing the required testing effort for system reliability assessment. Due to various reasons, not all components are directly testable in practice. The missing information of untestable components poses significant challenges to the accurate evaluation of system reliability. This paper proposes a sequential accelerated life testing (SALT) design framework for system reliability assessment of systems with untestable components. In the proposed framework, system-level tests are employed in conjunction with component-level tests to effectively reduce the uncertainty in the system reliability evaluation. To minimize the number of system-level tests, which are much more expensive than the component-level tests, the accelerated life testing (ALT) design is performed sequentially. In each design cycle, testing resources are allocated to component-level or system-level tests according to the uncertainty analysis from system reliability evaluation. The component-level or system-level testing information obtained from the optimized testing plans is then aggregated to obtain the overall system reliability estimate using Bayesian methods. The aggregation of component-level and system-level testing information allows for an effective uncertainty reduction in the system reliability evaluation. Results of two numerical examples demonstrate the effectiveness of the proposed method.

Resource Allocation for System Reliability Assessment Using Accelerated Life Testing

2019 ◽  
Vol 142 (3) ◽  
Author(s):  
Kassem Moustafa ◽  
Zhen Hu ◽  
Zissimos P. Mourelatos ◽  
Igor Baseski ◽  
Monica Majcher
Keyword(s):  
Resource Allocation ◽  
System Reliability ◽  
Failure Time ◽  
Reliability Assessment ◽  
Accelerated Life Test ◽  
Assessment Process ◽  
System Level ◽  
Component Level ◽  
Accelerated Life ◽  
Failure Time Distributions

Abstract Accelerated life test (ALT) has been widely used to accelerate the product reliability assessment process by testing a product at higher than nominal stress conditions. For a system with multiple components, the tests can be performed at component-level or system-level. The data at these two levels require different amount of resources to collect and carry different values of information for system reliability assessment. Even though component-level tests are cheap to perform, they cannot account for the correlations between the failure time distributions of different components. While system-level tests can naturally account for the complicated dependence between component failure time distributions, the required testing efforts are much higher than that of component-level tests. This research proposes a novel resource allocation framework for ALT-based system reliability assessment. A physics-informed load model is first employed to bridge the gap between component-level tests and system-level tests. An optimization framework is then developed to effectively allocate testing resources to different types of tests. The information fusion of component-level and system-level tests allows us to accurately estimate the system reliability with a minimized requirement on the testing resources. Results of two numerical examples demonstrate the effectiveness of the proposed framework.

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.

Resource Allocation for System Reliability Analysis Using Accelerated Life Testing

2019 ◽  
Author(s):  
Kassem Moustafa ◽  
Zhen Hu ◽  
Zissimos P. Mourelatos ◽  
Igor Baseski ◽  
Monica Majcher
Keyword(s):  
Resource Allocation ◽  
System Reliability ◽  
Failure Time ◽  
Reliability Assessment ◽  
Accelerated Life Test ◽  
Assessment Process ◽  
System Level ◽  
Component Level ◽  
Accelerated Life ◽  
Failure Time Distributions

Abstract Accelerated life test (ALT) has been widely used to accelerate the product reliability assessment process by testing product at higher than nominal stress conditions. For a system with multiple components, the tests can be performed at component-level or system-level. The data at these two levels require different amount of resources to collect and carry different values of information for system reliability assessment. Even though component-level tests are cheap to perform, they cannot account for the correlations between the failure time distributions of different components. While system-level tests can naturally account for the complicated dependence between component failure time distributions, the required testing efforts are much higher than that of component-level tests. This research proposes a novel resource allocation framework for ALT-based system reliability assessment. A physics-informed load model is first employed to bridge the gap between component-level tests and system-level tests. An optimization framework is then developed to effectively allocate testing resources to different types of tests. The information fusion of component-level and system-level tests allows us to accurately estimate the system reliability with a minimized requirement on the testing resources. Results of one numerical example demonstrate the effectiveness of the proposed framework.

Sequential Accelerated Life Testing Design for System Reliability Analysis With Untestable Components

2018 ◽  
Author(s):  
Zhen Hu ◽  
Zissimos P. Mourelatos
Keyword(s):  
System Reliability ◽  
Reliability Assessment ◽  
Reliability Evaluation ◽  
Accelerated Life Testing ◽  
System Level ◽  
Life Testing ◽  
Component Level ◽  
Accelerated Life ◽  
Framework System ◽  
Testing Design

Testing of components at higher-than-nominal stress level provides an effective way of reducing the required testing effort for system reliability assessment. Due to various reasons, not all components are directly testable in practice. The missing information of untestable components poses significant challenges to the accurate evaluation of system reliability. This paper proposes a sequential accelerated life testing (SALT) design framework for system reliability assessment of systems with untestable components. In the proposed framework, system-level tests are employed in conjunction with component-level tests to effectively reduce the uncertainty in the system reliability evaluation. To minimize the number of system-level tests which are much more expensive than the component-level tests, the accelerated life testing design is performed sequentially. In each design cycle, testing resources are allocated to component-level or system-level tests according to the uncertainty analysis from system reliability evaluation. The component-level or system-level testing information obtained from the optimized testing plans are then aggregated to obtain the overall system reliability estimate using Bayesian methods. The aggregation of component-level and system-level testing information allows for an effective uncertainty reduction in the system reliability evaluation. Results of two numerical examples demonstrate the effectiveness of the proposed method.

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