Fault Diagnosis of Simplified Fault Trees using State Transition Diagrams

Jiao, Mingyue; Huang, Yanhong; Shi, Jian; Cai, Fangda; Lin, Rongfeng

doi:10.1109/apsec51365.2020.00009

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Li et al [4] put forward a decision tree based on the method for line fault cause analysis by mining fault causes' time distribution law and the hierarchical structure from fault history, which improved the analysis speed, provided more detailed and accurate conclusions, and illustrated the effectiveness of the algorithm by providing examples. Jiao M et al [5] transformed the fault tree (FT) into a state transition diagram (STD) to simplify the FT. Multiple fault combinations can be diagnosed and unnecessary operations can be reduced by using the converted STD to handle intermediate and basic events separately when performing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of High-Voltage Drive Motor Systems in Terms of the Polymorphic Bayesian Network

Zheng

Jiang

et al. 2023

Mathematics

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The reliability of the high-voltage drive motor system for pure electric commercial vehicles is in premium demand. Conventional reliability based on fault tree analysis methods is not suitable for the quantitative assessment of polymorphic systems. As an example of a pure electric commercial vehicle, this paper combines polymorphic theory and Bayesian theory to establish a polymorphic Bayesian network model of a high-voltage drive motor system in terms of a polymorphic fault tree and to quantitatively judge the system. The polymorphic Bayesian network (BN) model can accurately depict the high-voltage drive motor system’s miscellaneous fault states and solve the top event’s probability in every state, also solving the system and drawing the consistent conclusion that the presence of abrasive particles, high-temperature gluing, moisture, and localized high temperatures are the system’s weak links by solving the critical importance, probabilistic importance, and posterior probability of the underlying event, which provides a theoretical reference for structure contrive optimization and fault diagnosis. This is extremely important in terms of improving pure electric commercial vehicles’ high-voltage drive motor systems.

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Section: Introductionmentioning

confidence: 99%