Infrastructure asset owners develop strategies to maximize asset availability and minimize economic losses due to the failure of critical assets. Infrastructure asset management policy involves the strategic development of inspection, repair and renewal plans. The policy evaluation support decisionmakers in assessing its impact on the reliability of the assets. This paper attempts to show the procedure to evaluate these policies through Generalized Stochastic Petri-Net with fuzzy parameters. The stochasticity allows representation of randomness in a system and fuzziness support incorporation of uncertain information from experts. It can be used periodically to estimate the type and number of actions required to maintain assets in good operating condition over the planning horizon. The methodology is demonstrated on the evaluation of maintenance policy of supplementary drive and slide chair in railway switches & crossing.

Intelligent fault detection 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 detect 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.