Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests

Li, Mengwei; Zhou, Ying; Jia, Limin; Qin, Yong; Wang, Zhipeng

doi:10.3390/app13148226

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…The boundary scan test is an advanced technique for digital circuit fault detection, which is widely used in large-scale digital circuit tests due to its fast and efficient testing advantages [1][2][3][4][5]. However, with the increase in IC density and complexity, the scale of boundary scan test vectors also increases rapidly, which leads to a decrease in test efficiency and fault detection precision [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Boundary Scan Test Vectors Optimization Method Based on Improved GA-AO* Approach Considering Fault Probability Model

Su,

Guo,

Luo

et al. 2024

Applied Sciences

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The generation of test vectors is a key technique that affects the efficiency and fault detection rate of the boundary scan test. Aiming at the local optimal solution problem of the current common test vectors generation algorithm, this paper proposes a test vectors generation algorithm based on improved GA-AO* model, through which the test vectors are generated by using the idea of heuristic search and backtracking correction. In order to speed up the heuristic search, this paper designed a heuristic function with both prior and posterior parameters to describe the influence of typical faults on the failure probability index of the test vectors. At the same time, this paper used a genetic algorithm (GA) to determine the specific values of the posterior parameters iteratively. Finally, through theoretical analysis and physical verification, compared with the test vector generated by the traditional method, the test vector generated by this method is optimized on the prior failure probability index and performs better in the physical experiment.

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Section: Introductionmentioning

confidence: 99%

A Boundary Scan Test Vectors Optimization Method Based on Improved GA-AO* Approach Considering Fault Probability Model

Su,

Guo,

Luo

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

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Integrated testability modeling method of complex systems for fault feature selection and diagnosis strategy optimization

Zhu,

Sun,

Yan

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part O:

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The efficacy of system fault diagnosis is intricately linked with the testability design of the system, particularly in intricate systems encompassing numerous components exhibiting diverse failure modes. However, current research shows a dearth of effective testability models and algorithms for generating diagnostic strategies in multi-valued attribute systems (MVAS) with uncertainty. To address this, the present paper introduces a novel method for testability modeling for complex systems. This approach incorporates signal features in lieu of raw sensor signals within the testability modeling process and accounts for the uncertainties surrounding test outcomes. The context of complex MVAS, characterized by inherent uncertainty, the paper proposes a novel method for constructing a four-value dependency matrix (D-matrix). Furthermore, the paper presents a novel sequential diagnosis strategy optimization approach based on a heuristic evaluation function for the multivalued D-matrix with uncertainty. The proposed methodology has been rigorously validated using a real-world case study involving an aero-engine fuel metering device system. Comparative experiments show that the proposed method can achieve better diagnostic performance in the shortest time.

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Fault Diagnosis of Unmanned Aerial Systems Using the Dempster–Shafer Evidence Theory

Liu,

Zhou,

Zhu

et al. 2024

Actuators

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Unmanned aerial systems (UASs) find diverse applications across military, civilian, and commercial sectors, including military reconnaissance, aerial photography, environmental monitoring, precision agriculture, logistics, and rescue operations, offering efficient, safe, and cost-effective solutions to various industries. To ensure the stable and reliable operation of UASs, fault diagnosis is essential, which can enhance safety, and minimize potential risks and losses. However, most existing fault diagnosis methods rely on a single physical quantity as the primary information source or solely consider fault data at a single moment, leading to challenges of low diagnostic accuracy and limited reliability. Aimed at this problem, this paper presents a fault diagnosis method based on time–space domain weighted information fusion for UASs. First, the Gaussian fault model is constructed for the data with different fault features in the space domain. Next, the weighted coefficient method is used to generate the basic probability assignment (BPA) by matching the fault data with the Gaussian fault model. Then, the Dempster’s combination rule, which enables the Dempster–Shafer (D-S) evidence theory, is adopted to fuse the generated BPAs. Based on this, the pignistic probability transformation is performed to determine the fault type. Finally, numerical results demonstrate the effectiveness of the proposed fault diagnosis method in accurately identifying the fault types of UASs.

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Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests

Cited by 3 publications

References 31 publications

A Boundary Scan Test Vectors Optimization Method Based on Improved GA-AO* Approach Considering Fault Probability Model

A Boundary Scan Test Vectors Optimization Method Based on Improved GA-AO* Approach Considering Fault Probability Model

Integrated testability modeling method of complex systems for fault feature selection and diagnosis strategy optimization

Fault Diagnosis of Unmanned Aerial Systems Using the Dempster–Shafer Evidence Theory

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