Traditional fault identification algorithms applied to catenary dropper suffer from various problems due to its small contact area. These problems include misidentification and lower recognition rate of the faulty dropper. Compared with the traditional convolutional neural network, the vector is utilized as the input of the capsule network (CapsNet) for the first time, which can well retain the feature information such as the direction and angle of the target, and is more suitable for identifying the dropper under complex background. Therefore, this paper proposes a dropper fault identification algorithm based on improved capsule network. The convolutional layer of traditional 9 × 9 capsule network is simplified through 1 × 1 reduction layer and 3 × 3 convolutional layer, and the optimization algorithm is adopted for parameter optimization to shorten the training weight time. At the same time, the output can retain more information such as direction and angle, which can accurately identify the breakage and falling of current carrying broken. Thus, in order to better improve the accuracy and real-time of detecting the fault dropper from a running train operation, a dropper fault identification algorithm based on an improved CapsNet is proposed in this paper. Experimental results show that the improved CapsNet is well-suited for fault identification of catenary dropper, as it can effectively remove the interference caused by the complex background on the dropper image, and identify the image containing the faulty dropper with a higher recognition rate.
PID controller plays an important role in Automatic Train Operation systems. Many researches have been done on the PID parameters estimation. However, the traditional models simplified the characteristics of the trains in different speeds and operation modes. It was difficult to overcome the problems with different kinds of algorithms based on the traditional models, especially the accuracy requirement of the stopping distance. In this paper, a new model of the PID controller was proposed based on the practical train operation stages. And the different response time delay was considered besides of the transmission time delay. With this new model, an Improved Fruit Fly Optimization Algorithm was applied. The tracking performance were greatly improved both on running distance and real time. The simulation results showed a better performance than the traditional methods.
In the marine environment, the wear of carbon brushes in the brush slip ring system of doubly-fed generators is accelerated and the replacement frequency is relatively high. In this paper, an experimental platform is built to simulate the working environment of the carbon brush slip ring system in the marine environment, and the factors causing the accelerated wear of carbon brushes are explored, so as to make suggestions on operation and maintenance of the brush slip ring system. The composition of the oxidation film on the contact surface of the brush slip ring has been quantitatively analyzed to provide a basis for determining the content of the oxidation film.
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