Health Warning and Fault Diagnosisof Pulverizing System Based on Syncretic Similarity

Authors

Songming Jiao, Department of Automation, North China Electric Power University, Baoding 071003, China;

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.

Recommended Citation

Jiao, Songming (2019) "Health Warning and Fault Diagnosisof Pulverizing System Based on Syncretic Similarity," Journal of System Simulation: Vol. 30: Iss. 2, Article 28.
DOI: 10.16182/j.issn1004731x.joss.201802028
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss2/28

First Page

595

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201802028

Last Page

604

CLC

TP206+.3;TP277

Recommended Citation

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

DOI

10.16182/j.issn1004731x.joss.201802028