热力发电2024，Vol.53Issue(1)：145-153,9.DOI:10.19666/j.rlfd.202306103
融合多层感知机和多项式拟合的大数据平台风机故障诊断
Fan fault diagnosis of big data platform based on multilayer perceptron and polynomial fitting
摘要
Abstract
To enhance the whole process safety of fan operations and ensure accurate fault diagnosis and long-term production income of thermal power plants,predicting these risk issues is crucial to enhance the safety of the unit.In this paper,we proposed a fan fault diagnosis model of big data platform that integrates multilayer perceptron and polynomial fitting.The fan early warning model was established by multilayer perceptron and polynomial fitting modeling technology,and integrated into the big data platform to find abnormalities which were difficult to find manually during the operation of the fan.By combining data mining with mechanism analysis and feature value knowledge base,the parameters boundary information of fan stall could be excavated,the stall boundary conditions of the fan were accurately configured under various working conditions,and a stall boundary condition diagram was created.By combining those informations with normal operating conditions,the early stall zone can be obtained.Finally,a fault diagnosis model that covers the entire working condition of the fan can be established.Utilizing the comprehensive big data platform that covers,circulates,and maintains fan operation data,a system of intelligent fan patrol model was constructed.The intelligent patrol disk model which replaces the operator was then used to monitor and diagnose the fan running state regularly,which can achieve accurate and safe diagnosis of fan faults,minimize the fault incidence and maximize the personnel reuse rate.关键词
大数据平台/风机/故障诊断/多层感知机/多项式拟合Key words
big data platform/fan/fault diagnosis/multilayer perceptron/polynomial fitting引用本文复制引用
吴青云,赵如宇,李昭,姚智,蔺奕存,孟颖琪,高景辉,何信林,高奎,赵晖,谭祥帅,郭云飞,牛利涛..融合多层感知机和多项式拟合的大数据平台风机故障诊断[J].热力发电,2024,53(1):145-153,9.基金项目
中国华能集团有限公司标准项目(HNBZ22-Q023)Standard Project of China Huaneng Group Co.,Ltd.(HNBZ22-Q023) （HNBZ22-Q023）
