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.

Subway train's collector shoes obtain electric energy from the power supply system, and the sliding contact between collector shoes and third rail could cause wear, which has a significant influence on the safety of train. Besides, the wear intensity is related to multiple factors. This paper presents a method of collector shoes wear based on the train operating mileage, makes prediction of the current degree of collector shoes wear, and tests the effectiveness of this method according to the experiment.

With the continuous improvement of the level of science and technology, conventional substations have basically realized the digitization and intelligence of inspections, and photovoltaic power stations need to be at the forefront of development. At present, there are still some photovoltaic power plants that have not yet gotten rid of the shackles of the traditional inspection mode, which restricts the development of photovoltaic power plants. Therefore, in order to promote the intelligent inspection process of photovoltaic power plants, a targeted inspection robot overall framework was designed, including integrated functions such as navigation, observation, and robotic arms. And a simple test was carried out by means of a comparative test, which affirmed the capability and economic advantages of the inspection robot in the photovoltaic power station, which can be vigorously promoted and applied. It can be seen that through this research, favorable conditions are provided for the digital transformation of the substation business.