机电工程技术2025，Vol.54Issue(18)：8-12,34,6.DOI:10.3969/j.issn.1009-9492.2025.00002
数据驱动的排烟风机故障诊断方法综述
Review of Data-driven Smoke Extraction Fan Fault Diagnosis Methods
摘要
Abstract
Crucial roles are played by smoke extraction fans in multiple fields of social production and daily life,and especially in the field of emergency rescue,the stability of their performance is directly related to economic interests and public safety.Therefore,the rapid identification and resolution of faults in smoke exhaust fans is of great significance for ensuring production safety.For the two core links in the data-driven fan fault diagnosis framework,namely signal feature extraction and fault identification,the relevant research achievements and progress at home and abroad in recent years have been reviewed.Meanwhile,the application cases of fan fault diagnosis based on different data-driven methods have been summarized.In addition,in view of the shortcomings in the current research,the adoption of multi-signal feature fusion diagnosis and the fan fault diagnosis system based on semi-supervised learning as future research directions has been proposed.And corresponding development suggestions have been provided.关键词
风机故障/特征提取/故障识别/机器学习Key words
fan fault diagnosis/feature extraction/fault identification/machine learning分类
信息技术与安全科学引用本文复制引用
许敬能,李方玉,赵举,夏宇栋,常凯..数据驱动的排烟风机故障诊断方法综述[J].机电工程技术,2025,54(18):8-12,34,6.基金项目
上海市质检院科研项目(KY-2022-9-JD) （KY-2022-9-JD）
