基于深度SSD改进模型的传动设备状态在线监测研究

Research on On-line Monitoring of Transmission Equipment Status Based on Improved Deep SSD Model

摘要:

针对现有传动设备在线监测算法存在的检测精度地、效率差等问题，提出一种基于改进SSD网络模型的在线检测算法。先对故障集进行预处理，通过滤波调制、共振解调等环节滤除原始故障集的噪声干扰；以VGG-16为基础设计了SSD网络结构，同时增加了辅助卷积层和预测层；对SSD网络模型进行改进，引入了注意力机制模块和特征增强模块，改善模型各层的数据共享性能同时提高了模型的数据训练效率；基于通道拼合方式对故障数据进行多尺度特征融合，并优化SSD模型的各层金字塔结构，以更好的匹配先验框及选择最佳的损失函数。实验结果显示，提出算法的传动设备故障检测率达到98.8%，同时算法的检测效率也优于现有算法。

Abstract:

Aiming at the problems of accuracy and efficiency of existing on-line monitoring algorithms for transmission equipment, an on-line detection algorithm based on improved SSD network model is proposed. Firstly, the fault set is preprocessed, 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 auxiliary convolution layer and prediction layer are added. To improve the SSD network model, the attention mechanism module and feature enhancement module are introduced to improve the data sharing performance of each layer of the model and improve the data training efficiency of the model. The multi-scale feature fusion of fault data is carried out based on the channel fusion method, and the pyramid structure of each layer of SSD model is optimized to better match the prior frame and select the best loss function. Experimental results show that the transmission equipment fault detection rate of the proposed algorithm is 98.8%, and the detection efficiency of the proposed algorithm is better than the existing algorithm.