A novel application of image processing for the detection of rail surface RCF damage and incorporation in a crack growth model

The paper presents the development of an intelligent image processing algorithm capable of detecting fatigue defects from images of the rail surface. The links between the defect detection algorithm and 3D models for rail crack propagation are investigated, considering the influence of input parameters (materials, vehicle characteristics, loading conditions). The dynamic behaviour at the wheel-rail interface resulting in contact forces responsible for stressing and straining the rail material are imported from vehicle dynamics simulations. The integration of the simulated results from vehicle dynamics, contact and fracture mechanics models offer more reliable estimation of the stress intensity factors (SIF). Also the sensitivity analysis related to materials, vehicle characteristics, and loading conditions will provide further understanding of the factors that influence crack propagation in rails such as shear stresses, hydraulic pressure, fluid entrapment and squeeze film effect. This novel application of image processing for the detection of rail surface rolling contact fatigue (RCF) damage and automatic incorporation in a crack growth model represents an important contribution to the development of modern techniques for non-destructive rail inspection. This will result in improved planning/scheduling of future rail maintenance (e.g. rail grinding, renewal), less disruptions and reduced track maintenance costs in rail industry

Defect Detection and Localization of Nonlinear System Based on Particle Filter with an Adaptive Parametric Model

A robust particle filter (PF) and its application to fault/defect detection of nonlinear system are investigated in this paper. First, an adaptive parametric model is exploited as the observation model for a nonlinear system. Second, by incorporating the parametric model, particle filter is employed to estimate more accurate hidden states for the nonlinear stochastic system. Third, by formulating the problem of defect detection within the hypothesis testing framework, the statistical properties of the proposed testing are established. Finally, experimental results demonstrate the effectiveness and robustness of the proposed detector on real defect detection and localization in images

Anomaly Detection in Textured Surfaces

Detecting anomalies in textured surfaces is an important and interesting problem that has practical applications in industrial defect detection and infrastructure asset management with a lot of potential financial benefits. The main challenges in this task are that the definition of anomaly changes from domain to domain, even noise can differ from the normal data but should not be classified as an anomaly, lack of labelled datasets and a limited number of anomalous instances. In this research, we have explored weak supervision and network-based transfer learning for anomaly detection. We developed a technique called AnoNet, which is a novel and compact fully convolutional network architecture capable of learning to detect the actual shape of anomalies not only from weakly labelled data but also from a limited number of examples. It uses a unique filter bank initialization technique that allows faster training. For a HxWx1 input image, it outputs a HxWx1 segmentation mask and also generalises to similar anomaly detection tasks. AnoNet on an average across four challenging datasets achieved an impressive F1 Score and AUROC value of 0.98 and 0.94 respectively. The second approach involved the use of network-based transfer learning for anomaly detection using pre-trained CNN architectures. In this investigation, fixed feature extraction and full network fine tuning approaches were explored. Results on four challenging datasets showed that the full network fine tuning based approach gave promising results with an average F1 Score and AUROC values of 0.89 and 0.98 respectively. While we have successfully explored and developed a method each for anomaly detection with weak supervision and supervision from a limited number of samples, research potential exists in semi-supervised and unsupervised anomaly detection

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

On modelling of the structural integrity of rails and crossings

Railway crossing is a vital asset for the railway system. Its complex geometry subjects it to high impact and dynamic loads from passing wheels, which can result in premature component failure. This study aims to enhance our understanding of dynamic interaction and increase the reliability of crossings. A finite element method (FEM) model of the wheel-crossing dynamic contact interaction has been successfully developed and utilised to investigate the impact of crossing material behaviour, wheel speed, and crossing angle on the vertical impact force exerted on the crossing nose by the passing wheels on the through route. It was observed that crossing with strain hardening behaviour exhibit less deformation and higher vertical impact force compared with perfect plastic crossing. Furthermore, a larger crossing angle leads to higher vertical impact forces, particularly at high wheel speed (> 100 km/h). The extended finite element method (XFEM) was employed, and the XFEM model demonstrated good results in predicting crack growth in rail steel (R260) specimens under a three-point bending static test. However, when simulating crack growth in rail, which has a more complex geometry and mechanism, the current model relies on the traction-separation law, which yields excessively high predicted vertical static forces. This model should be improved by considering factors such as longitudinal traction, lateral traction, and shear stress. The acoustic emission (AE) technique, combined with direct current potential drop (DCPD) measurements, was employed to monitor crack growth in cast manganese steel samples under a three-point bending fatigue test in the laboratory. The results show that the AE technique is successful in monitoring crack growth in this controlled environment. Finally, the concept of level 1 fitness-for-service analysis has been adapted for a maintenance action plan on defective rails. This plan relies on the RCF crack depth and surface crack length criteria. In the future, an advanced level of fitness-for-service analysis, employing more sophisticated calculation techniques, will be developed to enhance the predictive maintenance strategy of railway infrastructure managers

Computer Vision-Based Automatic Railroad Crossing Monitoring and Track Inspection

Currently, there are many imminent challenges in the railroad infrastructure system of the United States, impacting the operation, safety, and management of railroad transportation. In this work, three major challenges which are overcrowded traffic congestion at the grade crossing, low-efficiency and accuracy on inspection of missing or broken rail track components, and dense rail surface defects without quantification, respectively are studied. The congested railroad grade crossing not only introduces significant traffic delays to travelers but also brings potential safety concerns to the first responders. However, limited studies have been devoted on developing an intelligent traffic monitoring system which is significant to deliver real-time information to the travelers and the first responders to improve the traffic operation and safety at the railroad grade crossing. Except to improve the railroad safety related with travelers and the first responders in the first half, the rest of this dissertation focuses on the track safety related to railroad track components and surface defects. The missing or broken components such as spikes, clips, and tie plates can endanger the safety and operation of railroads. Even though various types of inspection approaches such as ground penetrating radar, laser, and LiDAR have been implemented, the operation needs rich experience and extensive training. Meanwhile, track inspections still heavily rely on manual inspection which is low-accurate, low-efficient, and highly subjective. Moreover, rail surface defects negatively impact riding comfort, operational safety, and could even lead to train derailments. During the past decades, there have been many efforts to detect rail surface defects. Unfortunately, previous approaches for detecting and quantifying of rail surface defects are also limited by the high requirements of specialized equipment and personnel training. The main focus of this work is to design and develop computer vision models to address the technical and practical challenges mentioned above. To cope with each challenge, different models including the object detection model, the instance segmentation model, and the semantic segmentation model have been successfully designed and developed. To train, validate, and test different models, three customized image datasets based on the traffic videos at the grade crossing, railroad component images, and dense rail surface defects images have been built. Specifically, a dense traffic detection net (DTDNet) is developed integrating the Transformer Attention (TA) module for better modeling of global context information and the learning-to-match detection head for optimizing object detection and localization using a likelihood probability fashion. A unique grade crossing traffic image dataset including congested and normal traffic during both daytime and nighttime is established. The proposed DTDNet and other state-of-the-art (SOTA) models have been trained, tested, and compared. The proposed DTDNet outperforms other SOTA models in the test cases. Regarding the automatic track components inspection, the real-time instance segmentation model and the YOLOv4-hybrid model have been designed, trained, tested, and evaluated. The first public rail components image database has been built and released online. Compared to the original YOLACT model and the Mask R-CNN model, the training performance has been improved with the improved instance segmentation model. The detection accuracy on the bounding box and the mask has been improved and the inference speed can achieve the real-time speed. With respect to the YOLOv4-hybrid model, it outperforms other SOTA models on the training performance and the field tests with missing or fake rail track components. As for the rail surface defect inspection and quantification, the optimized Mask R-CNN model and the newly proposed lightweight Deeplabv3Plus model using Lovász-Softmax loss (LDL model) have been trained, tested, evaluated, and compared on our rail surface defects image database. Experimental results confirm the robustness and superiority of our model on defect segmentation. Besides, an algorithm is proposed to quantify rail surface defect severities at different levels using our rail surface defects image data. Overall, this dissertation helps to improve the railroad safety by developing and implementing advanced computer vision-based models for better tracking monitoring and inspections

Incorporating automated rail fatigue damage detection algorithms with crack growth modelling

This thesis examines the feasibility of incorporating Non Destructive Testing (NDT) of rail surface damage by means of combining image processing with damage prediction models. As rail traffic and adherence to safety measures become increasingly strict on the network, the associated maintenance cost of rail infrastructure must be kept at a minimum. Proactive maintenance is crucial to maintaining the competitive advantage of rail transport. A considerable amount of research has been done on improving the practical tediousness associated with popular condition monitoring techniques in rail industry e.g. Ultrasonic, and Eddy current method. This thesis aims to fill the gap of yet to be explored benefit, of combining detection and prediction of RCF damage. This research project will contribute to the rail industry by simplifying maintenance operations and support decision making. In this thesis, a summary of existing image-based NDT and crack growth models is presented as a foundation on which the novel application is built.It could be said that similar research mainly focuses on quantifying severity of damage without predicting crack behaviour. The simulated results of the proposed image processing algorithm confirm superiority of local illumination invariant enhancement, multi-window segmentation, and cascaded feature extraction. The influential parameters of these methods are consistent within each image data set but differ across all sets. This is observed to be as a result of difference in environmental and reflection properties of acquired images.A sensitivity analysis of the proposed algorithm on data set 2 suggests a non-linear relationship between severity of damage and pixel mean intensity including variance. Taking to account fracture mechanics aspect of this thesis, the influence of crack geometry on growth rate and path has been established by case study of newly initiated and critically grown cracks. It was further established that larger cracks are observed to grow faster than smaller ones. In addition, the influence of track curve radius and supporting structures on wheel rail contact dynamics is well understood from the structural mechanic’s tests related to contact forces and bending moment. These translate to increase or decrease in contact stresses, strains, and the propagation rate of defects. Unlike other predictive models, the method developed in this thesis focuses on replicating the actual surface condition of the rail prior to estimating the fracture parameters (using detailed 3D Finite Element model) that dictate residual life of the rail asset. The model makes it possible to combine two separate maintenance activities i.e. detection and prediction without inducing down time of the service. A direct impact of this novel application is the utilisation of the actual crack boundary for prediction of fracture behaviour. It is insinuated that stress distribution of actual crack boundary differs from elliptical equivalent assumptions. Further work would include improving detection aspect of the novel application to avoid intersecting boundary coordinates, which are not readily imported into the Linear Elastic Fracture Mechanics (LEFM) prediction model. It is also beneficial to expand the prediction aspect of the research work to include influence of neighbouring cracks and fluid entrapment for more flexible analysis of other environmental and contact conditions. To improve on current work, it will be useful to conduct laboratory investigations on the influence of Image Acquisition System (IAS) light source in relation to illumination inequality within the captured image. Also fracture mechanics experimental validation can be used to assert the accuracy of the metho