Research on Fault Detection Method of Mineral Powder Production Line Based on Information Fusion | Zendy

Zhantao Wang | Zendy; Yi Luo | Zendy; Yong Tang | Zendy; Xinjia Lu | Zendy; YanKai Zhang | Zendy

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Research on Fault Detection Method of Mineral Powder Production Line Based on Information Fusion

Author(s) -

Zhantao Wang,

Yi Luo,

Yong Tang,

Xinjia Lu,

YanKai Zhang

Publication year - 2020

Publication title -

iop conference series materials science and engineering

Language(s) - English

Resource type - Journals

eISSN - 1757-899X

pISSN - 1757-8981

DOI - 10.1088/1757-899x/740/1/012068

Subject(s) - information fusion , fault (geology) , production (economics) , bayesian probability , data mining , computer science , process (computing) , fusion , production line , fault detection and isolation , artificial intelligence , algorithm , pattern recognition (psychology) , machine learning , engineering , mechanical engineering , linguistics , philosophy , actuator , seismology , geology , operating system , economics , macroeconomics

By analysing the uncertainty of information in the production process of the mine, and the diversity of the fault detection components of the production equipment. Combining the trend of big data development, comparing and analysing various fault diagnosis methods, a Bayesian information fusion algorithm based on improved learning process is proposed. The traditional Bayesian information fusion algorithm is combined with the learning idea of migration algorithm to obtain an optimized Bayesian fault detection algorithm. This algorithm can minimize the waste and loss of data and improve the detection accuracy.

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