基于FastICA算法的齿轮箱故障诊断方法  

Gearbox Fault Diagnosis Based on the Algorithm of FastICA

作　　者：王细洋[1] 胡贵锋[1] 

机构地区：[1]南昌航空大学,江西南昌330063

出　　处：《南昌航空大学学报（自然科学版）》2013年第1期73-76,共4页Journal of Nanchang Hangkong University(Natural Sciences)

基　　金：航空科学基金(2010ZD56011);人事部留学人员科研择优基金(DB200903036);南昌航空大学科技创新团队项目(EB200906296)

摘　　要：在齿轮箱故障诊断中,传感器采集的振动信号由噪声和齿轮信号叠加组成。为了分离出有用的齿轮信号,以便对齿轮箱故障进行正确诊断,提出了采用基于负熵的FastICA算法对齿轮箱振动信号进行分离的方法,即对箱体故障信号的时域与频谱进行分离分析和相似度分析。通过将FastICA的分析结果与齿轮箱实际故障进行对比,发现采用基于负熵的FastI-CA法进行信号分离不仅有助于正确地判断故障特征,而且可增强待分析故障信号的强度,还可诊断其故障形式,是故障诊断的预处理方法。In fault diagnosis, vibration signals include noise signal and the gear box signal. In order to separate the useful gear signals so that the correct diagnosis of a gear box failure is done, a new method based on the negative entropy algorithm of FastICA was pro-posed so as to separate gear box vibration signals. The method analyses the vibration signal of domain and spectrum, getting separated signals of their own. The results show that the method can not only help to correctly judge the failure characteristics, and but also enhance the analysis strength of the fault signal, and diagnosize the form of its failure, which indicates that the method can be used as the pretreatment method of the fault diagnosis.

关 键 词：盲源分离 故障诊断 负熵 FASTICA 信号预处理 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置] TP274[自动化与计算机技术—控制科学与工程]