摘要
为对车载CNG气瓶因裂纹引起的安全事故进行控制,研究了气瓶裂纹声发射信号的特征。分析了气瓶失效的机理,提出在实验室条件下拉伸金属试件的方法,并对之进行模拟与声发射信号采集。通过对信号的分析显示出其时域与频域分析在特征分析时所具有的局限性,从而引出小波分析的信号分析方法,结果得出不同类型的声发射信号在相同频率段内所占的能量比例系数不同的结论。因此利用信号能量比例作为该信号的特征值,并在此基础上进行神经网络识别,取得了理想的结果,表明基于小波的金属材料信号特征能够对信号进行较好地表征。
In order to reduce the accident rate of CNG pressure vessel, acoustic emission signal features of the vessel when under tensile stresses were investigated. According to the failure process of CNG cylinder, tensile metal specimens were used to simulate the process and to collect the signals. Through the analysis of signal, the limitations 0f characteristic analysis on time domain and frequency domain were pointed out. So the signal was analyzed by the method of wavelet. Results indicated that different types of signals had different energy coefficient ratio in the same frequency. According to the characteristics, the energy ratio characteristic was used to characterize the AE signal. Recognition results indicated that this recognition method of BP network could well identify the signal type.
出处
《无损检测》
2012年第6期8-11,16,共5页
Nondestructive Testing
基金
四川省过程装备高校重点实验室资助项目(GK200909)
关键词
声发射检测
CNG气瓶
金属试件
特征值
小波分析
Acoustic emission testing
CNG gas cylinders
Metal specimens
Characteristic value
Wavelet analysis
