金属材料声发射信号特征提取方法

AE signal feature extraction method of metal materials

试图通过对声发射信号的检测实现对水轮机转轮叶片金属疲劳裂纹的在线监测。利用美国PAC公司SAMOS声发射检测系统采集到声发射的各种参数;针对大型水轮机现场环境的情况,选用了四种声发射信号。通过BP神经网络和模式识别结合的方法,设计特征提取器来提取金属材料疲劳声发射特征信号。比较神经网络输入参数对输出结果的灵敏度,选择出一些对分类识别最有效的特征参数;并采用可分离性判据进一步验证其正确性。最后,在13个声发射特征参数中,质心频率、计数、持续时间、上升时间、平均信号电平等五个参数的特征最为显著,可以用于识别现场环境下的声发射信号。

The attempt of using acoustic emission signal detection to carry out the turbine blades metal fatigue crack on-line monitoring has been made. Acoustic emission signal parameters are acquired by using SAMOS Acoustic Emission Testing System of the American PAC Corporation; In actual large turbine environment, four kinds of acoustic emission signals are selected. Combining BP neural network and pattern recognition, a feature extractor is designed to extract the metal fatigue characteristics of acoustic emission signals. Compared the sensitivity of input parameters to output results of neural network, several most effective parameters are chosen for identification and classification; and the separableness criterion is used further confirm its accuracy. Finally, in total 13 characteristic parameters of acoustic emission, five para-meters, such as centroid frequency, counts, duration, rise time and average signal level（ASL） can be most notably used to identify acoustic emission signal in actual environment.