基于ANFIS的锅炉故障诊断研究  

Research on Boiler Fault Diagnosis Based on ANFIS

作　　者：张维 ZHANG Wei(Guodian Inner Mongolia Dongsheng Thermal Power Co.,Ltd.,Ordos,Inner Mongolia 017000,China)

机构地区：[1]国电内蒙古东胜热电有限公司,内蒙古鄂尔多斯017000

出　　处：《自动化应用》2024年第12期123-125,共3页Automation Application

摘　　要：随着现代社会对电力需求的不断增加和先进火力发电系统的日益复杂,提高系统的性能和可靠性变得越来越重要。故障诊断系统可在噪声测量条件下自动补偿不利影响。为提高直流锅炉的过程监控能力和故障诊断的准确性,基于各测量传感器间的强相关作用,提出基于6种ANFIS的数据驱动故障诊断方法,每个ANFIS分类器均可诊断一种特定锅炉系统故障。通过不同场景的测试仿真,验证了所提ANFIS在噪声测量条件下对锅炉6种主要故障诊断的有效性和性能。With the increasing demand for electricity in modern society and the increasing complexity of advanced thermal power generation systems,improving system performance and reliability has become increasingly important.The fault diagnosis system can automatically compensate for adverse effects under noise measurement conditions.To improve the process monitoring capability and accuracy of fault diagnosis of DC boilers,a data-driven fault diagnosis method based on six ANFIS is proposed based on the strong correlation between various measurement sensors.Each ANFIS classifier can diagnose a specific boiler system fault.The effectiveness and performance of the proposed ANFIS in diagnosing six main faults of boilers under noise measurement conditions were verified through testing and simulation in different scenarios.

关 键 词：故障诊断 数据驱动 自适应神经模糊推理系统 锅炉 传感器相关性 

分 类 号：TF351[冶金工程—冶金机械及自动化]