Finite State Machine Model of Fault Diagnosis for Distribution System under Time Sequence Constraints

Shang, Lei; Wang, Lei; Niu, Lin; Yan, Zhang; Li, Hongbo; Ma, Mengchao; Zhou, Boxi

doi:10.1109/icpsasia48933.2020.9208588

Cited by 2 publications

(1 citation statement)

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“… 38 The common limitation of the above two methods is that they are qualitative analysis methods, which rely on system knowledge and expert experience, and cannot give quantitative conclusions. Realizing the importance of time in fault diagnosis, Shang 39 introduced a method of finite state machine model of fault reasoning for distribution system under time sequence constraints. And aiming at the problem of fault diagnosis when there are only a few labeled samples in the large amount of data collected during the operation of rotating machinery, Ye et al 40 proposed a fault diagnosis method based on knowledge transfer in deep learning.…”

Section: Introductionmentioning

confidence: 99%

Chemical Process Alarm Root Cause Diagnosis Method Based on the Combination of Data-Knowledge-Driven Method and Time Retrospective Reasoning

Song

Liu²,

Dong

et al. 2022

ACS Omega

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Due to the abrupt nature of the chemical process, a large number of alarms are often generated at the same time. As a result of the flood of alarms, it largely hinders the operator from making accurate judgments and correct actions for the root cause of the alarm. The existing diagnosis methods for the root cause of alarms are relatively single, and their ability to accurately find out complex accident chains and assist decision making is weak. This paper introduces a method that integrates the knowledge-driven method and the data-driven method to establish an alarm causal network model and then traces the source to realize the alarm root cause diagnosis, and develops the related system modules. The knowledge-driven method uses the hidden causality in the optimized hazard and operability analysis (HAZOP) report, while the data-driven method combines the autoregressive integrated moving average model (ARIMA) and Granger causality test, and the traceability mechanism uses the time-based retrospective reasoning method. In the case study, the practical application of the method is compared with the experimental application in a real petrochemical plant. The results show that this method helps to improve the accuracy of correct diagnosis of the root cause of the alarm and can assist the operators in decision making. Using this method, the root cause diagnosis of alarm can be realized quickly and scientifically, and the probability of misjudgment by operators can be reduced, which has a certain degree of scientificity.

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Section: Introductionmentioning

confidence: 99%