Software and machine learning tools for monitoring railway track switch performance

Trackside data logging hardware is often used in the UK, and increasingly elsewhere in the world, to record and transmit processed condition data from track switching equipment (points) in order to gauge asset health. This paper presents a novel implementation of three tools which can be used together to make the analysis and handling of this data easier. The first of these tools is a statistical classifier which automatically assigns labels to the process data. The classifier is trained using historical data containing examples of events of interest, such as recordings taken when maintenance activity or failures have developed. In practice, the labels are used to pre-filter the data, to bring swift attention to events of interest, and to automatically create categorised datasets which can be used to analyse historical performance. Two different types of classifier are presented: a Gaussian Naïve Bayes classifier and a neural network classifier. The second tool is a simple pattern recognition algorithm which can determine when the different phases of mechanical operation in a single track switch movement occur, for example locking, unlocking, and moving. The final tool is a statistical technique which is used to extract simple features from the data and raise alarms if they indicate poor track switch performance. The effectiveness of these tools is tested using real world data taken from three different railways.