模糊模式识别在轮箍超声横波探伤中的应用

Application of fuzzy pattern recognition in ultrasonic transverse wave detection of wheel flaws

轮箍是铁路机车运行的重要部件,在制造和使用过程中出现的各种危害性缺陷会严重威胁到列车的行驶安全。用超声无损检测缺陷,回波的识别易受轮箍标记、闸瓦、轮轨接触点及表面波等多种因素的干扰。在超声横波探伤基础上,可将模糊模式识别应用到机车轮箍的无损检测中。以内燃机车轮箍为实验检测对象,使用了多个标准人工伤模拟轮箍自然缺陷。通过提取缺陷回波频域相关特征建立典型缺陷的模糊子集,并运用基于贴近度的择近原则对未知缺陷进行分类识别。实验结果证明了该方法有效。对同一缺陷重复检测的正确识别率达92.5%。

In manufacturing and running of locomotive wheels, various flaws may occur, threatening safety of the train. In ultrasonic non-destructive testing of wheel flaws, the wheel brake, sign, touch point, and even the surface wave will influence the recognition of flaw echoes. Using a gas engine wheel as a test object, typical artificial flaws were made to simulate natural flaws. Based on the transverse wave detection technique, a fuzzy pattern recognition method is applied to uhrasonic transverse wave detection of wheel flaws. Frequency echoes are extracted, from which fuzzy sets of typical flaws domain features of the received flaw are constructed. The concept of similarity degree and the principle of choosing the near ones are used. Experimental results show that the method is efficient and can achieve satisfactory performance. In repeated detection of the same flaw, the recognition rate is 92.5%, providing a useful reference to practical applications.