Multi-Segment Deep Convolution Neural Networks for Classification of Faults in Sensors at Railway Point Systems

Intelligent fault diagnosis by sensor data can ensure the reliability and availability of critical infrastructures. Switches and crossings (S&C) are one of the most important assets of railway networks. They divert trains in different directions by shifting the position of switch rail by point operating equipment (POE). The sensors record the electrical current drawn by the motor in POE. The extraction of features from time-series sensor data enables the detection of faults in POE. This paper proposes a deep learning model to diagnose faults in railway POE without the need for preprocessing the raw time-series data. It is based on 1-D convolution neural network. The architecture of the proposed deep learning network consists of three types of layers. The first layer is called the local convolution layer. It consists of three 1-D convolution layer to extract local temporal features from three non-overlapping segments of time-series data of different operating phases of POE. The second layer is fully-connected convolution layer. It extracts global temporal features. And the last layer is the output layer, it provides the binary output of fault or fault free for a given sensor data. The result shows that this framework can classify fault with 95.60% accuracy.