Artificial intelligence based condition monitoring of rail infrastructure

The design cycle of the rail condition monitoring system (CMS) consists of problem investigation, treatment design, and validation. The aim of the project is to improve the rail maintenance by timely reporting the incipient rail defects. Ontime and appropriatemaintenance results in reduction ofmaintenance cost, short down-time and high service availability. A massive data has been collected from railway system through various sensors but the connection between the sensors data and the rail condition is not known. Moreover, currently the maintenance strategies are triggered mostly based on human inspection. Therefore an automated rail CMS need to be developed that makes intelligent decisions using the data and helps in initiating a timely maintenance process. The rail defects need to be detected at their earliest stageswhich could otherwise lead to severe defects and cause rail failure. Therefore the aimed system will help in carrying out predictive maintenance of the rail infrastructure. The detailed description of the problem
is discussed in chapter 1. The solution for the design problem is build upon the need and requirements of the stakeholders and system. Everything that the stakeholders
expect from this solution is included in the list of requirements. Moreover, requirements at the system level are also identified and aimed to be achieved. A comprehensive list of requirements is given table 2.1 in chapter 2.
The design of the rail CMS is based on the train axle box acceleration (ABA) data, that is used by the machine learning (ML) pipeline for information retrieval about rail condition. The designed ML pipeline for rail CMS is illustrated in figure 3.1 of chapter 3. The pipeline consists of ABA pre-processing, extraction of features by using time domain analysis, and anomaly detection algorithm for detecting irregular patterns in ABA. The algorithm for anomaly detection is presented in detail in chapter 4. The validation process is based on the comparison of the actual rail defects and anomalies detected by the algorithm in ABA data. The flowchart for the validation process is shown in figure 5.1 in chapter 5. Video images of rail infrastructure are utilized for performing the validation process. The visible rail defects in the images are manually labelled and feed to the validation model for comparison. The performance metrics such as hits, mishits and false alarms etc. are calculated using the validation model. The design and user guide for the graphical
user interface (GUI) of rail CMS is covered in chapter 6 that explains various components in the layout and discusses the inputs and outputs of the system. Finally the discussion, conclusions, and recommendations are presented in chapter 7 of the thesis report.