Application of wireless sensor networking techniques for train health monitoring

The use of wireless sensor networking in conjunction with modern machine learning tech- niques is a growing area of interest in the development of vehicle health monitoring (VHM) system. This VHM system informs forward -looking decision making and the initiation of suitable actions to prevent any future disastrous events. The main objective of this thesis is to investigate the design and possible deployment of a less expensive, low-power VHM system for railway operations. 

The performance of rail vehicles running on railway tracks is governed by the dynamic behaviours of railway bogies, especially in the cases of lateral instability and track irregular- ities. The proposed VHM system measures and interprets vertical accelerations of railway wagons attached to a moving locomotive using a wireless sensor network (WSN) and ma- chine learning techniques to monitor lateral instability and track irregularities. Therefore this system enables reduction of maintenance and inspection requirements of railway systems while preserving the necessary high levels of safety and reliability.
The thesis is divided into three major sections. First, an energy -efficient data commu- nication system is proposed for railway applications using WSN technology. Initially, a conceptual design of sensor nodes with appropriate hardware design is presented. Then an energy -efficient adaptive time division multiple access (TDMA) protocol is developed, further reducing the power consumption of the data communication system. This data communication system collects data from sensor nodes on the wagons and passes it to the locomotive. Secondly, a data acquisition model involving machine learning techniques is used to further reduce power consumption, computational load and hardware cost of the overall condition monitoring system. Only three sensor nodes are required on each railway wagon body to collect sufficient data to develop a VHM system instead of four sensor nodes in an existing system. Finally, a VHM system is developed to interpret the vertical acceler- ation behaviour of railway wagons using popular regression algorithms that predicts typical dynamic behaviour of railway wagons due to track irregularities and lateral instability. 

To summarise, this study introduces wireless sensor networking technology that enables the development of an energy-efficient, reliable and low cost data communication system for railway operational applications. By using machine learning techniques, an energy -efficient VHM system is developed which can be used to continuously monitor railway systems, particularly railway track irregularities and derailment potential with integrity. A major benefit of the developed system is a reduction in maintenance and inspection requirements of railway systems.