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Research Papers: Design Automation

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

[+] Author and Article Information
Zhen Hu

Department of Industrial and
Manufacturing Systems Engineering,
University of Michigan-Dearborn,
2340 Heinz Prechter Engineering Complex
(HPEC),
Dearborn, MI 48128
e-mail: zhennhu@umich.edu

Zissimos P. Mourelatos

Professor
Mechanical Engineering Department,
Oakland University,
Room 402D, 115 Library Drive,
Rochester, MI 48309
e-mail: mourelat@oakland.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 10, 2018; final manuscript received June 16, 2018; published online July 24, 2018. Assoc. Editor: Mian Li.

J. Mech. Des 140(10), 101401 (Jul 24, 2018) (13 pages) Paper No: MD-18-1119; doi: 10.1115/1.4040626 History: Received February 10, 2018; Revised June 16, 2018

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.

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Figures

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Fig. 2

Overview of the proposed SALT framework

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Fig. 1

Illustration of system with untestable component: (a) a mechanical system and (b) a system with untestable component

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Fig. 3

Flowchart for the evaluation of f(t|ssys, nsys, a0sys, θ, θu)

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Fig. 4

Implementation procedure of the proposed SALT framework

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Fig. 5

System configuration of a mixed system

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Fig. 10

A four-joint robot system: (a) kinematic diagram and (b) reliability block diagram

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Fig. 6

Comparison of SALT and DS methods (best case)

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Fig. 7

Comparison of SALT and DS methods (worst case)

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Fig. 8

Posterior distribution updating history of untestable component (component 2) over iterations

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Fig. 9

Comparison of testing cost distributions to reach to the stopping criterion

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Fig. 11

Comparison of SALT and DS methods (25 executions)

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Fig. 12

Comparison of testing cost distribution to reach the stopping criterion

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