Supervised Machine Learning Approach for Detecting Missing Clamps in Rail Fastening System from Differential Eddy Current Measurements

Abstract

The rail fastening system forms an integral part of rail tracks, as it maintains the rail in a fixed position, upholding the track stability and track gauge. Hence, it becomes necessary to monitor their conditions periodically to ensure safe and reliable operation of the railway. Inspection is normally carried out manually by trained operators or by employing 2-D visual inspection methods. However, these methods have drawbacks when visibility is minimal and are found to be expensive and time consuming. In the previous study, the authors proposed a train-based differential eddy current sensor system that uses the principle of electromagnetic induction for inspecting the railway fastening system that can overcome the above-mentioned challenges. The sensor system includes two individual differential eddy current sensors with a driving field frequency of 18 kHz and 27 kHz respectively. This study analyses the performance of a machine learning algorithm for detecting and analysing missing clamps within the fastening system, measured using a train-based differential eddy current sensor. The data required for the study was collected from field measurements carried out along a heavy haul railway line in the north of Sweden, using the train-based differential eddy current sensor system. Six classification algorithms are tested in this study and the best performing model achieved a precision and recall of 96.64% and 95.52% respectively. The results from the study shows that the performance of the machine learning algorithms improved when features from both the driving channels were used simultaneously to represent the fasteners. The best performing algorithm also maintained a good balance between the precision and recall scores during the test stage.

An Investigation of Railway Fastener Detection Using Image Processing and Augmented Deep Learning

“…This allows their use in complex environments, such as those involving stones, water, oil, machine fluids and snow. The differential eddy current inspection was able to detect the fastener state with a precision and recall of 96.64% and 95.52%, respectively [6]. The results presented in the previous studies were based on controlled measurements carried out along the heavy haul line in the northern part of Sweden, where the likelihood of disturbances were minimal.…”

“…In Sweden, the tracks are covered with snow for the majority of the year and would thus require additional rail surface treatment or a removal process that adds to the expenses of the railroad companies. One possible solution to overcome this difficulty and ensure safe, sustainable and reliable rail operation, is by combining automated visual inspection with non-destructive testing such as eddy current sensors [6,33] for fastener inspection. The presence of non-conductive materials (such as ballast and snow) in the sensor-to-target gap do not affect the eddy current sensors.…”

“…The down time arising from such maintenance and renewal of networks is responsible for nearly half of all the delays to passengers, reducing the service quality. In Sweden, an average of 572.7 h and 670.3 h of delays are incurred due to failure of components in track and switches and crossings (S&C), respectively [6]. The track and its components need to be inspected periodically to avoid such delays due to failure, and to ensure safe and reliable operation.…”

Unsupervised Machine Learning for Missing Clamp Detection from an In-Service Train Using Differential Eddy Current Sensor

“…The downtime arising from these M&R of networks is responsible for nearly half of all the delays to passengers. In Sweden, an average of 572.7 h per year of delay is incurred due to the failure of components in the railway track [6]. To mitigate such delays and to ensure safe, sustainable, and reliable operation, the track and its components need to be inspected frequently.…”

“…Eddy current (EC) testing is one of several NDT methods that work on the principle of electromagnetism, which is used for examining metallic components. In earlier research, the authors proposed a train-based differential eddy current sensor [6,29] for fastener inspection that can overcome the major challenges associated with the visual inspection. EC sensors work on the principle of electromagnetism and are not affected by the presence of nonconductive materials in the sensor-to-target gap.…”