Research on Data-Driven Fault Diagnosis Technology of Cloud Test

Kang, Weijie; Xiao, Jiyang; Kong, Xiaoruo

doi:10.1007/978-981-13-9406-5_28

Cited by 1 publication

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“…The method is simple and accurate, and the accuracy and robustness of the results are high. So at present, the industry and the academia are gradually turning to data-driven methods for fault classification (Kang et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

Liu

2020

IJICC

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PurposeFor the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing. This paper proposes a fault classification algorithm based on Gaussian mixture model (GMM), which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.Design/methodology/approachThe algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy (MYT) decomposition under each normal distribution in the GMM. Then, the weight value of GMM is used to calculate weighted classification value of fault detection and separation, and by comparing the actual control limits with the classification result of GMM, the fault classification results are obtained.FindingsThe experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.Originality/valueThe proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.

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Section: Introductionmentioning

confidence: 99%

Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

Liu

2020

IJICC

View full text Add to dashboard Cite

show abstract

Research on Data-Driven Fault Diagnosis Technology of Cloud Test

Cited by 1 publication

References 0 publications

Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

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