噪声与振动控制Issue(3)：165-169,5.DOI:10.3969/j.issn.1006-1335.2014.03.035
基于多维振动特征的滚动轴承故障诊断方法
Fault Diagnosis Method of Rolling Bearings Based on Multi-dimensional Vibration Features
摘要
Abstract
Extracting the time-domain or the frequency-domain features of vibration signals for analysis is a conventional method for rolling bearings fault diagnosis. But the effects of this diagnosis method need to be improved. In this paper, taking the multi-dimensional vibration characteristic parameters in time-domain and frequency-domain as the indexes and the correctness rate of historical diagnosis as the parametric weight, the features of fault-free rolling bearings and the features of rolling bearings with ball fault, inner and outer race faults are extracted and the faults are identified. It shows that the multi-dimensional vibration characteristic in time-frequency domains is the assemblage of single features. BP neural network is used for intelligent fault classification of signals according to the time-domain feature (TDF) parameters, IMF energy moment (IEM), wavelet package energy moment (WPEM) and multi-dimensional features respectively. Results of the diagnoses are compared one another. The experiment results verify that using the multi-dimensional feature in time and frequency domains to evaluate the rolling bearing faults is accurate and efficient. This method can be applied in the field of rolling bearing fault diagnosis.关键词
振动与波/多维特征/BP神经网络/故障诊断/滚动轴承Key words
vibration and wave/multi-dimensional feature/BP neutral network/fault diagnosis/rolling bearing分类
通用工业技术引用本文复制引用
付云骁,贾利民,季常煦,姚德臣,李文球..基于多维振动特征的滚动轴承故障诊断方法[J].噪声与振动控制,2014,(3):165-169,5.基金项目
国家高技术研究发展计划（863计划)(2011AA110506；2011AA110503) （2011AA110506；2011AA110503）
