Event pattern recognition of distributed optical fiber sensing system based on FES-RDB-CNN and Voting classifier combination

The event pattern recognition technology becomes an important research direction of distributed fiber optic vibration sensor. In this paper, an event pattern recognition scheme based on Feature enhanced and simplified RDB (FES-RDB)-CNN and Voting classifier combination (VCC) is proposed and applied to event pattern recognition for Sagnac distributed fiber sensing system. The FES-RDB proposed in this paper is a new residual dense block (RDB) that replaces the convolution block in the RDB with the residual unit in the 34-layer residual nets (ResNet-34) and replaces the ReLU activation function in the ResNet-34 with the Leaky ReLU activation function. By introducing FES-RDB in the feature extraction stage of conventional CNN, the capability of high-dimensional feature extraction, transmission and reuse of neural network is greatly improved. The three-dimensional map obtained by the t-SNE (t-distributed Stochastic Neighbor Embedding) algorithm shows that, FES-RDB makes the data points of different types of events have significantly farther distances, more distinct boundaries, and higher aggregation of event data points of the same type. Using the event pattern recognition scheme proposed in this paper, the average recognition accuracy of 9 types of events reaches 99.46%. Therefore, the event pattern recognition scheme based on FES-RDB-CNN+VCC has excellent performance in practicability and recognition accuracy, and has a good application prospect.