Health Warning and Fault Diagnosisof Pulverizing System Based on Syncretic Similarity

doi:10.16182/j.issn1004731x.joss.201802028

Abstract

Abstract: It is an arduous task to know health statusof a pulverizing system in power plant by monitoring these parameters simultaneously and the fault diagnosing process is complicated.An approach based on syncretic similarity is presented which is suitable for industrial system’s health warning and fault diagnosis.The syncretic similarity couples anew type of weighted mahalanobis distance based on principal component analysiswith an improved weighted sine similarity. The approachhas self-learning ability.Central parameterswhich are used to compute similarity can be modified along with the operation process.Simulation resultsshow that the method is suitable for online application because of its high accuracy, fast classification, high real-time performance, reliabilityand simple structure.

Key words: fault diagnosis, similarity, pulverizing system, health warning, mahalanobis distance

CLC Number:

JiaoSongming. Health Warning and Fault Diagnosisof Pulverizing System Based on Syncretic Similarity[J]. Journal of System Simulation, 2018, 30(2): 595-604.

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