The lifetime of wire rope is crucial in industry manufacturing, mining, and so on. The damage can be detected by using appropriate nondestructive testing techniques or destructive tests by cutting the part. For broken wires classification problems, this work is aimed at improving the recognition accuracy. Facing the defects at the exterior of the rope, a novel method for recognition of broken wires is firstly developed based on magnetic and infrared information fusion. A denoising method, which is adopted for magnetic signal, is proposed for eliminating baseline signal and wave strand. An image segmentation method is employed for parting the defects of infrared images. Characteristic vectors are extracted from magnetic images and infrared images, then kernel extreme learning machine network is applied to implement recognition of broken wires. Experimental results show that the denoising method and image segmentation are effective and the information fusion can improve the classification accuracy, which can provide useful information for estimating the residual lifetime of wire rope.

中文翻译：

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基于磁和红外信息的钢丝绳断丝定量无损检测

钢丝绳的使用寿命对于工业制造，采矿等至关重要。可以通过使用适当的非破坏性测试技术或通过切割零件进行破坏性测试来检测损坏。对于断线分类问题，这项工作旨在提高识别精度。面对绳索外部的缺陷，首先基于磁和红外信息融合，提出了一种识别断丝的新方法。提出了一种用于磁信号的去噪方法，以消除基线信号和波束。图像分割方法用于分割红外图像的缺陷。从磁图像和红外图像中提取特征向量，然后应用核极限学习机网络实现断线识别。