Condition Detection of Overhaul Platform Based on Image Processing

EMU(Electric Multiple Units) maintenance platform is one of the key equipments of the motor Train base and EMU application, provides convenient for EMU daily maintenance and inspection, maintenance and repair. In the process design of the equipment, hydraulic crossover and other methods are adopted to meet the needs of the maintenance personnel on different types of EMUs. Therefore, the tipping boards are set up on the actual platform and protection network to ensure the safety of the workers' reaching the top. The EMUs usually use a four line storehouse, and the number of tipping plates is hundreds of them. Most of them are reversing /receiving by cylinder driving, and a large number of flip plates are distributed in the workshop. Besides, the state of the retractable boards and the faults have important influence on the operation. Therefore, it is very important to monitor the state of the flip plate. At present, it is more dependent on the monitoring and controlling system of the mechanical stroke switch in China. Due to the state of the hardware and the stability of the system, the method has the risk of omission and false alarm in some cases. Meanwhile, the switch signal can not directly display the real-time state of the tipping board. Therefore, the present invention provides a high-definition network camera image processing board state detection system based on double plate state real-time monitoring and detection, enhance the safety of EMU maintenance platform. The real-time image acquisition, calculating the specified texture information area gray co-occurrence matrix, generating feature vector descriptor, using support vector machine (SVM) classification method for feature vector classification, training classifier, and use the classifier to recognize the flap, and locate the flap position. And SVM is a novel method of machine learning evolving from statistics. SVM presents many own advantages in solving machine learning problems such as small samples, nonlinearity and high dimension. In this paper, the image texture SVM classification method construct feature vectors through the extraction of image gray level co-occurrence matrix texture information. The metioned texture imformation include gray-level co-occurrence matrix energy, contrast and entropy. And the gray level co-occurrence matrix reflects the image direction, adjacent interval and the change in value of integrated information. The results show that the image texture SVM classification method can effectively combine the method of template matching for the identification of condition detection of overhaul platform..