Open AccessCoal mill fault diagnosis based on Gaussian process regression
Author(s) -
Ling Zhu,
Shuangbai Liu,
Deli Zhang,
Xiaozhi Qiu,
Weiqing Zhou
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/332/4/042034
Subject(s) - kriging , fault (geology) , mill , regression analysis , gaussian process , fault detection and isolation , regression , gaussian , process (computing) , engineering , coal , data mining , computer science , statistics , artificial intelligence , mathematics , machine learning , mechanical engineering , physics , quantum mechanics , seismology , waste management , actuator , geology , operating system
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.