摘要
低速重载设备突发故障难于识别,一旦发生,损失巨大。振动监测技术虽可以作为设备维护的重要手段, 但常规的频谱分析无法准确提取低速轴上的故障特征。对实时监测的振动数据,采用小波分解技术可以获得必要 的低频段信息。某个时段内的信号经小波变换后所定义的小波分层突变系数,可以作为判别低转频微冲击故障隐 患的特征值,而且该系数趋势图还可有效刻画出故障部位的劣化过程:对同一组监测数据,分别采用细化谱技术 和小波分解+FFT的复合信号处理技术进行比较分析,结果表明,由于FFT分析的局限性,细化谱无法准确识别 出故障原因及部位,而后者采用复合信号处理方法提取的故障特征频率对应的振幅变化剧烈得多,此法有助于低 速重载设备早期故障的准确识别。
The sudden fault on the low speed and heavy duty equipments are very difficult to recognize and it can bring about great loss. Though the equipments can be maintained by the vibration monitoring technology, the fault information on the low speed shaft can＇t be easily picked through the frequency spectrum analysis. The necessary information on the low frequency range can be obtained by wavelet analysis on the monitoring data. The sultation coefficient of wavelet decomposition on the vibration signal in some time range can be regarded as the characteristic value to judge the fault. Furthermore, the cofficients can capture the developing process of the fault. For the same series monitoring data, the refinable spectrum analysis and FFT analysis compounding wavelet decomposition are both used to complete the comparative research. The achievements show that the refinable spectrum method can＇t predict the fault reason and location. On the other hand, after the composite signal processing through wavelet analysis and FFT technique, the vibration amplitudes variation of the characteristic frequency of the hidden fault are very intense. As a result, it is very helpful to precisely recognize the early fault on the low speed and heavy duty equipments.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2005年第12期222-227,共6页
Journal of Mechanical Engineering
基金
北京市科委(H030330050110)北京工业大学博士科研启动基金(KZ0107200382
00138)资助项目。
关键词
低速重载
频谱分析
小波分析
Low-speed heavy-duty Spectrum analysisWavelet analysis
