基于神经网络的超声导波钢杆缺陷识别  

RECOGNITION OF DEFECTS ON STEEL ROD USING ULTRASONIC GUIDED WAVES BASED ON NEURAL NETWORK

作　　者：吴斌[1] 齐文博[1] 何存富[1] 周进节[1] 

机构地区：[1]北京工业大学机电学院,北京100124

出　　处：《工程力学》2013年第2期470-476,共7页Engineering Mechanics

基　　金：国家自然科学基金项目(10972014;51075012);北京市教委项目(KM201010005003);北京市优秀博士论文指导教师项目(YB20091000502)

摘　　要：利用基于BP神经网络的缺陷识别算法,从不同实验条件下获得的信号样本中抽取特征量,对钢杆中不同深度和位置的径向裂纹进行了识别。首先,采用频率为235kHz激励轴对称纵向模态导波对钢杆中的径向裂纹进行了检测。实验表明,在235kHz时获得的超声导波信号含较单一的L(0,2)模态,避免了用L(0,1)模态检测小尺寸缺陷时检测能力较弱的问题,又减少了用轴对称纵向高阶模态检测缺陷时模态较多不易分辨缺陷回波的现象。其次,利用算法对钢杆中的径向裂纹进行识别。结果表明,在已有实验样本数下,缺陷识别算法从整体上很好地识别不同深度和位置的裂纹,识别正确率稳定在87%。A kind of defect recognition algorithm based on BP neural network is used to extract characteristic quantities from the samples obtained at different experimental conditions, and distinguish the different sizes and positions of radial cracks in steel rods. Firstly, the axisyrnmetric longitudinal guided waves mode in 235 kHz are excited to detect the radial cracks in a steel rod. Experimental results show that obtained guided waves contain quite single L（0,2） mode in 235 kHz, it avoids the problem of weaker detection capability using L（0,1） mode to detect small size defects, and reduces the difficulty to distinguish the defect echo because more modes involved when axisymmetric longitudinal high order modes are used to detect the steel rod. Secondly, the algorithm is used to recognize the radial cracks in a steel rod. Results show that the proposed defect recognition algorithm can identify different depths and positions of cracks well, and the correct rate of recognition has stabled at 87% in existing experimental samples.

关 键 词：超声导波 钢杆 缺陷识别算法 特征量 BP神经网络 

分 类 号：TB132[理学—物理] TB559[理学—应用物理]