Research on On-line Monitoring of Transmission Equipment Status Based on Improved Deep SSD Model
Aiming at the problems of low detection accuracy and low efficiency of existing online monitoring algorithms for transmission e-quipment,an online detection algorithm based on improved single shot detector(SSD)network model is proposed.Firstly,the fault set is pre-processed,and the noise interference of the original fault set is filtered by filtering modulation and resonance demodulation.The SSD network structure is designed based on VGG-16,and the auxiliary convolution layer and prediction layer are added.To improve the SSD network mod-el,the attention mechanism module and feature enhancement module are introduced to improve the each layer data sharing performance of the model and improve the data training efficiency of the model.The multiscale feature fusion of fault data is carried out based on the channel fu-sion method,and the pyramid structure of each layer of the SSD model is optimized to better match the prior frame and select the optimal loss function.The experimental results show that the transmission equipment fault detection rate of the proposed algorithm is 98.8％,and the fault detection rates of three traditional algorithms are 94.2％,93.6％and 93.7％,respectively.Meanwhile,the detection efficiency of the pro-posed algorithm is better than that of the traditional algorithm.
deep SSDtransmission equipmentOn-line monitoringauxiliary convolutiondata trainingprior frameloss func-tion
万方数据
维普
