Fan fault diagnosis of big data platform based on multilayer perceptron and polynomial fitting

WU Qingyun; MENG Yingqi; GAO Jinghui; HE Xinlin; GAO Kui; ZHAO Hui; TAN Xiangshuai; GUO Yunfei; NIU Litao; ZHAO Ruyu; LI Zhao; YAO Zhi; LIN Yicun

doi:10.19666/j.rlfd.202306103

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Fan fault diagnosis of big data platform based on multilayer perceptron and polynomial fitting

更新时间：2025-09-28

- Fan fault diagnosis of big data platform based on multilayer perceptron and polynomial fitting
- Thermal Power Generation Vol. 53, Issue 1, Pages: 145-153(2024)
- 作者机构：
  
  西安热工研究院有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202306103
  CLC： TM621;TP311.13
- Published：2024
- 稿件说明：
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WU Qingyun, MENG Yingqi, GAO Jinghui, et al. Fan fault diagnosis of big data platform based on multilayer perceptron and polynomial fitting[J]. Thermal power generation, 2024, 53(1): 145-153.
DOI：

WU Qingyun, MENG Yingqi, GAO Jinghui, et al. Fan fault diagnosis of big data platform based on multilayer perceptron and polynomial fitting[J]. Thermal power generation, 2024, 53(1): 145-153. DOI： 10.19666/j.rlfd.202306103.

摘要

为了提高火电厂送引风机运行的全程安全化、故障诊断准确化、生产收益长期化，将风险问题前置是提升机组运行安全性的关键。基于此，提出了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模型。采用多层感知机和多项式拟合建模技术建立风机预警模型，并将模型部署在大数据平台中，能及时发现风机运行期间人工难以发现的异常。采用数据挖掘、机理分析和特征值知识库相结合的方法，挖掘风机失速的参数边界信息，精准化配置各种工况的风机失速边界条件并绘制失速边界工况图，然后结合正常运行工况得出预警失速区间，最终建立覆盖风机全工况的故障诊断模型。利用大数据平台对风机运行数据全覆盖、全流通、全维护的优势，构建了基于大数据平台的风机智能巡盘模型体系，实现以智能巡盘模型代替运行人员对风机运行状态进行定期巡盘监视和诊断，达到风机故障的准确安全诊断、故障发生率最低化及人员复用率最大化的效果。

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.

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