NDLI: Coal mill fault diagnosis based on Gaussian process regression

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Coal mill fault diagnosis based on Gaussian process regression

Content Provider	Scilit
Author	Zhu, Longfei Liu, Shuangbai Zhang, Deli Qiu, Xiaozhi Zhou, Weiqing
Copyright Year	2019
Description	Journal: Iop Conference Series: Earth and Environmental Science A typical operating set of equipment can be obtained through cluster analysis of historical data. Two state monitoring models for HP medium speed coal mill are established based on Gaussian process regression and the similarity index calculated by this model can be used for measuring the operating status of HP mills. Finally a method for fault diagnosis of HP mill based on Gaussian regression modelling is proposed combined with fault diagnosis knowledge base of this HP mill. Taking the HP medium speed mill of a 660MW thermal power unit as an example, the real operating data is collected and used for modelling and analysis. Results shows that the equipment parameter estimation calculated by Gaussian process regression is accurate. It can be used for early-warning and diagnosed of equipment fault and also for practical engineering application.
Related Links	https://iopscience.iop.org/article/10.1088/1755-1315/332/4/042034/pdf
ISSN	17551307
e-ISSN	17551315
DOI	10.1088/1755-1315/332/4/042034
Journal	Iop Conference Series: Earth and Environmental Science
Issue Number	4
Volume Number	332
Language	English
Publisher	IOP Publishing
Publisher Date	2019-11-05
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Earth and Environmental Science Mining and Mineral Processing Gaussian Process Regression
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Physics and Astronomy Environmental Science

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