基于超声参数化和熵模型的汽车焊点质量识别

汽车焊点的超声波检测过程中,超声回波为非稳态信号,特征不易提取,同时焊点缺陷类型众多,导致汽车焊点质量的自动判定与识别比较困难。因此,提出一种基于超声信号参数化和判别熵模型的汽车焊点质量智能识别方法。通过建立汽车焊点超声回波信号的参数化模型,再基于EM算法思想,提出多回波超声信号的特征参数估计算法。根据提取的超声信号时频特征值,结合判别熵对特征值的有效性进行监督,提取最优特征值子集,最终实现汽车焊点质量类型的智能识别。焊点的实际检测结果验证了方法的有效性和识别的准确性。

Recognition of Vehicle Welding Spot Quality Based on Ultrasonic Parameterized and Entropy Model

In the process of ultrasonic testing of vehicle welding spot, ultrasonic echo is unsteady signal and it is not easy to extract the characters, at the same time many types of defects make automatic determination and recognition of welding spot quality become more difficult. Therefore, an intelligent method to recognize the quality of welding spot based on ultrasonic parameterized and J-divergence entropy model is proposed. By establishing parameterized model of ultrasonic echo signal and based on EM algorithm, a parameters estimation algorithm of ultrasonic echo signal is proposed. According to the time-frequency characteristics of ultrasonic echo and supervising the effectiveness of these characteristics by the J-divergence entropy to get the optimal characteristics subset. Use the method to achieve the recognition of welding spot quality. The actual test results of welds have verified the validity of this method and the accuracy of recognition.