A general enhancement method for test strategy generation for the sequential fault diagnosis of complex systems. (December 2022)

• Record Type: Journal Article
• Authors: Wang, Jingyuan; Liu, Zhen; Wang, Jiahong; Long, Bing; Zhou, Xiuyun
• Is Part Of: Reliability engineering & system safety. Volume 228(2022)
• Publication Date: 2022-12
• Subjects: Test sequence generation -- Multi-signal flow graph model -- Clustering -- Classification -- Error estimation; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108754
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: We present a general enhancement method that is suitable for most existing test strategy generation algorithms based on a multi-signal model. The performance of the test strategy generation of each algorithm is improved after enhancement. Using SVM and ECA*, the dimension of the dependency matrix can be reduced dynamically. The morphological parameters can be obtained by Monte Carlo simulation. The error can be estimated by classification accuracy, clustering and morphological parameters. Abstract: In order to improve the reliability, operational readiness and system safety of equipment, testability should be seriously considered in the design stage. As an important part of design for testability, test sequence generation is a binary identification problem because a minimal expected cost testing procedure must be developed in order to determine the amount of possible failure sources, if any, are present. Many algorithms have been proposed, but the generation time is long or the test cost is high when dealing with a large-scale dependency matrix. To address this issue, we propose a general enhancement method based on the SVM, the ECA* and the Monte Carlo. It can be applied to any existing algorithm and can effectively improve the performance. The available tests are classed based on the SVM according to the information of nodes, the ECA* is used to cluster states, and the morphological function of the test sequence is obtained through the Monte Carlo simulation. All this information is fused to dynamically adjust the scale of the dependency matrix and selected to modify the parameters. Experiments show that the existing algorithms have shorter calculation time and lower costs because the information is considered more comprehensively after enhancement.