基于小波包分解的风机故障诊断技术研究  

Study on Wavelet Packet Analysis-based Fault Diagnosis for Blower Fan

作　　者：刘晓波[1] 孙康[1] 

机构地区：[1]江西理工大学

出　　处：《金属矿山》2005年第1期56-58,共3页Metal Mine

基　　金：江西省科技厅科技攻关资助项目(200210200211);江西省教育厅科技资助项目(教赣计字[2001]387)资助。

摘　　要：小波包分析由于能对信号高、低频部分局部细化并保留原信号的时域特征,因而具有良好的时频局部化特性,能对非平稳信号进行有效识别,达到故障诊断的目的,在故障诊断领域得到越来越广泛的应用。风机运行时产生的信号大多是非平稳信号,将小波包分析技术用于其故障诊断具有实际意义。Wavelet packet analysis has good time-frequency localization features due to its capability of localizing and differentiating the high and low frequencies of a signl and keeping the time domain features of the original signal. As it can effectively identify the unstable signals to meet the requirements of fault diagnosis, wavelet packet analysis has aquired increasingly wider application in fault diagnosis field. The signals produced during the blower fan running are mostly unstable ones. Therefore, it is of practical significance to use wavelet packet analysis technology in the fault diagnosis of blower ~fan.

关 键 词：非平稳信号 低频 时域特征 小波包分解 小波包分析 细化 局部 故障诊断技术 领域 

分 类 号：TD235.1[矿业工程—矿井建设] TN911[电子电信—通信与信息系统]