A review on dependency matrix and its application in fault diagnosis

Li, Xiaosong; Cui, Weiwei; Li, Lian-Feng; Yang, Jann N.

doi:10.1109/phm-yantai55411.2022.9941798

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…Then, Shakeri [2] discussed system fault modeling for more complex systems. Recently, Li et al [3] reviewed the dependency matrix and its application in fault diagnosis, including the basics of dependency matrix generation, test selection methods, and fault diagnosis. In all, the popular D-matrix processing algorithms can be classified into three basic categories, that is Weight index for Fault Isolation (WFI) [4], Information Gain (IG) [5,6], and Weight index for Fault Detection (WFD) algorithm [4].…”

Section: Introductionmentioning

confidence: 99%

A Divide-and-Conquer Information Entropy Algorithm for Dependency Matrix Processing

2023

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Processing the system's dependency matrix is a core procedure for system diagnosis. However, the present algorithms for this job always try to maximize its fault isolation capability, which is thus not only unnecessary for the system that cannot be repaired in the field like weapons, but also generates low-efficient test sequence. To this end, the present work proposes a new processing algorithm for testability D-matrix, named Divide-and-Conquer Information Gain (DIG), targeting to those systems without strong fault isolation requirement. It combines the advantage of the classic algorithm Information Gain (IG) and Weight index for Fault Detection (WFD) by introducing a new entropy computing method considering the weight index for fault detection. To verify the advantage, generality and flexibility of the new algorithm, a D-matrix from a real system and random D-Matrixes are tested in the experiments, and measured by test sequence length, actual test cost and expected test cost. The result shows that DIG algorithm is 15.7% and 14.3% better than that of IG and WFD algorithm on the expected test cost metric, respectively.

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Section: Introductionmentioning

confidence: 99%

A Divide-and-Conquer Information Entropy Algorithm for Dependency Matrix Processing

2023

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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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A review on dependency matrix and its application in fault diagnosis

Cited by 2 publications

References 44 publications

A Divide-and-Conquer Information Entropy Algorithm for Dependency Matrix Processing

A Divide-and-Conquer Information Entropy Algorithm for Dependency Matrix Processing

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

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