Rail transportation is a sustainable mode of transportation and is a key enabler of the socio-economic development of modern society through passenger and freight services. 

Growth in overall transport demand has led to railways experiencing higher demand on operational capacity, service quality, and safety. However, an increase in traffic and load can lead to an increase in degradation of the components and thus cause a reduction in the infrastructure quality. Such degradation leads to failures of components, consequently resulting in a higher frequency of interventions for maintenance and renewal activities. The downtime arising from such maintenance and renewal of networks is a significant contributor to the delays incurred to the passengers. A plausible solution to attain higher operational capacity and quality of service with the existing infrastructure and minimize delays due to failure would be to inspect the track and its components frequently using in-service trains, operating in regular traffic. One of the crucial components in rail tracks is the rail fastening system, which acts as a means to fix the rails onto the sleeper, upholding the track stability and track gauge.  Failures of fasteners can increase wheel flange wear, reduce the safety of train operations, and may lead to derailment due to gage widening or wheel climb. In Sweden, the inspection of track fasteners is mainly carried out either manually by trained inspectors or by using measurement cars. Manual inspections are slow, costintensive, labour-intensive, pose safety issues for maintenance personal involved, and are prone to human errors. Inspections based on measurement cars are cost intensive and requires track possession and thus cannot be utilised frequently without compromising the operational capacity.  

Further, the adverse weather condition, especially in the north of Sweden for the majority of the year, limit regular fastener inspection that depends on such traditional inspection methods. 

The purpose of this project was to facilitate the development of an automated method for fastener inspection that can be carried out using vehicle-mounted measuring equipment operating in regular traffic. 

Firstly, a study was carried out to determine the effectiveness of automated visual-based solutions for fastener state detection. An anomaly detection model combining image processing techniques and deep learning algorithms was developed to detect the fastener state from rail images captured during the vision-based inspection. The model had a high capability of detecting the fastener state from the rail images. However, the model had difficulties detecting the fastener when there were instances of occlusions of fasteners due to the presence of snow and ballast stones and when the image brightness was low. 

In Sweden, specifically the northern part of it, the fastening systems are covered under snow for up to six months and thus can inhibit regular fastener inspections that rely on such automated visual inspection methods. To overcome the challenges associated with automated visual inspection systems for fastener state detection, an alternative inspection method using a differential eddy current measurement system (Lindometer) was investigated. Controlled field measurements were carried out along a heavy haul railway line in the north of Sweden to determine the effectiveness of the proposed measurement system. An anomaly detection model based on a supervised machine learning algorithm was developed to detect the fastener state from the controlled eddy current measurements.

The thesis was successfully defended by Praneeth Chandran on 7th April 2022 at the division of Operation and Maintenance Engineering, Luleå University of Technology. The grading committee for the dissertation included Prof. Rolf Dollevoet (Delft University of Technology, Netherlands), Prof Gopika Vinod (Homi Bhabha National Institute, India), Dr. Krister Wolff (Chalmers, Sweden), Prof. Kalevi Juhani Huhtala (Tampere University of Technology, Finland) and Prof. Uday Kumar (LTU, Sweden.