摘要
根据小波包多分辨原理,对齿轮减速器JZQ250内的轴承振动信号进行处理,在分析滚动轴承振动机理的基础上,利用小波包进行多尺度分解,根据信号与噪声随尺度增加的不同传播特性,低频部分代表信号的发展趋势,正常状态信号趋势单调递增,故障状态信号趋势则单调递减,初步判定轴承的运行状态是否正常。当判定为故障时,选择小波包分解重构后特殊层的信息进行频谱分析,提取其故障特征频率。经对大量实测数据的处理和分析,能够比较准确地识别和诊断出减速器的正常运行状态,内圈、外圈和保持架故障运行状态,具有一定的工程实用价值。
According to the principle of wavelet packet,the vibration signal of roller bearings in JZQ250 gear reducer was processed.On the basis of analysis of the vibration mechanism of roller bearings,with the multiscale decomposition by wavelet packets,running state is preliminarily judged according to different propagation characteristics of the signal and noise with the scale,the low frequency part represents development trends of signal,the normal is monotonically increasing and the fault is monotonically descending,the information of the special layer reconstructed by the wavelet packet is chosen to perform the frequency analysis and extract its characteristic frequency of the fault when the fault is made sure.Through processing and analysis of massive measured data,the method can accurately identify and diagnose different running states of a JZQ250 reducer including the normal state,and inner ring,outer ring and cage fault states.The results shows that the method has certain engineering practical value.
出处
《现代电子技术》
2010年第9期154-156,159,共4页
Modern Electronics Technique
基金
国家自然科学基金资助项目(50875247)
山西省研究生创新基金项目资助(2008072)
山西省自然科学基金项目资助(2009011026-1)
关键词
轴承
小波包分析
特征频率
故障诊断
roller bearing
wavelet packet analysis
characteristic frequency
fault diagnosis
