【摘要】 针对常用的时域和频域分析在诊断通风机故障时存在不能同时诊断出故障时间和类型的瓶颈问题,提出基于小波包分解的煤矿通风机故障诊断方法。在分析通风机故障特征的基础上,利用时频两域都具有表征信号特征能力的小波,对采集来的通风机振动信号进行小波包分解,利用分解的小波系数,在各个频段上进行小波信号重构,并计算信号各个频段的能量特征值,提取故障特征,诊断故障发生的时间和故障类型。经实际验证,小波包分解能将故障信号有效的划分到不同的频段内,而且时域和频域局部化特性好,能有效的诊断出通风机故障,具有良好的理论意义与工程应用价值。更多还原

【Abstract】 It is difficult to diagnose the time of faults occurrence and the type of faults in a coal mine ventilator at the same time using conventional time domain and frequency domain analysis methods. A new method of fault diagnosis for a coal mine ventilator is proposed in this paper,which is based on wavelet packet decomposition. Based on the analysis of ventilator’s fault characteristics,the vibration signal is decomposed into wavelet packet using the common signal characteristics in both time domain and frequency domain. Using the wavelet coefficient obtained,the wavelet signal is reconstructed at each frequency band,and the energy eigenvalue in each frequency band is calculated so as to diagnose the time of fault occurrence and the fault type. The case study shows that wavelet packet can effectively decompose the fault signal into different frequency domains and diagnose the ventilator’s fault,which is useful in theoretical and engineering applications.更多还原