旋转设备故障特征提取的研究
Study on Fault Feature Extraction for Rotating Equipment
陈彬 1全宇轩 1刘阁 2商芷萱 1张永昌1
作者信息
- 1. 华北科技学院 应急装备学院河北省矿山设备安全监测重点实验室,河北廊坊 065201
- 2. 华北科技学院 化工安全学院,河北廊坊 065201
- 折叠
摘要
旋转设备包括滚动轴承等设备,是工业生产中的核心设备之一,长期工作在重荷、高温等环境下,是故障发生率较高的设备类型.任何微弱故障的发生都可能会对设备的可靠运行与安全生产造成恶劣影响,因此针对旋转设备进行故障诊断研究十分重要.而对旋转设备的振动信号进行分析处理,提取其故障特征是目前对其进行故障诊断的重要途径.首先将故障特征提取方法划分为时域、频域、时频域分析和人工智能几个部分,分别详细阐述了国内外对旋转机械的故障特征提取方法,并对其基本理论与优缺点进行了分析,最后总结了各种故障特征提取方法的优点与局限性,并对故障特征提取领域未来发展趋势进行了展望,旨为研究人员带来新的启发.
Abstract
Rotating equipment,including rolling bearings and other equipment,is one of the core equipment in industrial production.It operates under heavy loads,high temperatures,and other harsh environments for extended periods of time,making it a type of equipment with a high failure rate.Any slight malfunction may have a negative impact on the equipment's reliable operation and safe production.Therefore,research on the fault diagnosis of rotating equipment is crucial.Analyzing and processing the vibration signals of the rotating equipment to extract fault features is currently an important method for fault diagnosis.Firstly,the methods for extracting fault features are divided into time domain,frequency domain,time-frequency domain analysis,and artificial intelligence.This article elaborates on the fault feature extraction methods for rotating machinery in China and abroad and analyzes the basic theories and advantages and disadvantages.Finally,the article summarizes the advantages and limitations of various fault feature extraction methods and prospects for future development trend in the field of fault feature extraction.The aim is to bring new inspiration to researchers.
关键词
旋转设备/滚动轴承/设备故障/时域分析/频域分析/时频域分析Key words
rotating equipment/rolling bearing/equipment failure/time domain analysis/frequency domain analysis/time-frequency domain analysis引用本文复制引用
基金项目
国家自然科学基金(51375516)
河北省高教学会规划重点课题(十四五)(GJXHZ2021-40)
出版年
2024
NETL
NSTL
万方数据