Research on equipment fault prediction expert system based on big data dimension reduction

doi:10.1088/1742-6596/1601/3/032045

	Research on equipment fault prediction expert system based on big data dimension reduction
	Zhu, Ningning 2; Song, Bin 1; Lu, Yan 2; Li, Yingtang 2; Zhu, Zhongzheng 2
	2020-08-17
会议日期	June 19, 2020 - June 21, 2020
会议地点	Jinan, Virtual, China
关键词	Backpropagation Big data Crude oil Dimensionality reduction Expert systems Fluid catalytic cracking Forecasting Fuzzy inference Fuzzy logic Fuzzy neural networks Heavy oil production Nonlinear control systems Pattern recognition systemsData dimension reduction Dimension reduction Error back propagation Heavy oil catalytic cracking Inspection and maintenance Petrochemical enterprise Physical information Prediction accuracy
卷号	1601
期号	3
DOI	10.1088/1742-6596/1601/3/032045
英文摘要	Due to the complex structure and massive volume of large equipment in petrochemical enterprises, it was very difficult to carry out inspection and maintenance work. The traditional expert system used a single knowledge base and reasoning machine, so the processing efficiency and prediction accuracy were low. In this paper, the dimensionality reduction which is used to process a large amount of physical information collected by the sensors, remove redundant components and extract main characteristic parameters. Meanwhile, combined with the powerful pattern recognition and judgment ability of fuzzy neural network, the forward reasoning mechanism and error back propagation keeping continuous training correction until meeting the accuracy requirements, the expert system gave the equipment fault prediction conclusion. Finally, taking main fan oil station circulating oil pump and motor of heavy oil catalytic cracking unit and nine stages of bearing damage as monitoring objects, the fault predictions was implemented by the following steps: data collection, feature extraction, dimension reduction of big data and expert system with the fusion of fuzzy neural network, and it is proved that the expert system with fuzzy neural network based on dimension reduction can greatly improve the convergence speed. © Published under licence by IOP Publishing Ltd.
会议录	Journal of Physics: Conference Series
会议录出版者	IOP Publishing Ltd
语种	英语
ISSN号	17426588
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/132628]
专题	计算机与通信学院
作者单位	1.Lanzhou Chemical Research Center, China Petroleum and Petrochemical Research Institute, Lanzhou; 730050, China 2.School of Computer and Communication, Lanzhou University of Technology, Lanzhou; 730050, China;
推荐引用方式 GB/T 7714	Zhu, Ningning,Song, Bin,Lu, Yan,et al. Research on equipment fault prediction expert system based on big data dimension reduction[C]. 见:. Jinan, Virtual, China. June 19, 2020 - June 21, 2020.