基于经验模态分解的锅炉风机滚动轴承故障诊断方法
Fault Diagnosis Method for Rolling Bearings of Boiler Fans Based on Empirical Mode Decomposition
赵振宇1
作者信息
- 1. 内蒙古大唐国际托克托电厂,内蒙古呼和浩特 010000
- 折叠
摘要
当前锅炉风机滚动轴承故障诊断识别结构设定多为单向,诊断识别的效率较低,故障诊断次数大幅度下降,因而提出基于经验模态分解的锅炉风机滚动轴承故障诊断方法的设计与实践分析.根据当前的诊断需求,先进行故障信号特征提取,采用多目标的方式,提升诊断识别的效率,设计多目标故障识别结构,以期提高故障诊断的准确性.
Abstract
The current structure for diagnosing and identifying faults in rolling bearings of boiler fans is mostly unidirectional,resulting in low diagnostic efficiency and a significant decrease in the number of fault diagnoses.Therefore,a design and practi-cal analysis of a fault diagnosis method for rolling bearings of boiler fans based on empirical mode decomposition is proposed.Based on the current diagnostic requirements,first extract fault signal features and adopt a multi-objective approach to im-prove the efficiency of diagnostic recognition.Design a multi-objective fault recognition structure to improve the accuracy of fault diagnosis.
关键词
经验模态分解/锅炉风机/滚动轴承/故障诊断Key words
empirical mode decomposition/boiler fan/rolling bearings/fault diagnosis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据