An embedded machine vision system for an in-line quality check of assembly processes

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A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis.

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%

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

Research on the propagation efficiency of ultrasonic guided waves in the rail

Ultrasonic guided waves (UGW) technique has the advantages of low detection frequency, long detection distance, strong anti-electromagnetic interference ability, and large coverage. Hence it has potential advantages in real-time detection of breakages in the rail. Based on the research background of UGW-based broken rail detection, this paper focuses on the characteristics optimization of piezoelectric ultrasonic transducers (PUTs) to improve the propagation efficiency of UGW in the rail. Due to the influence of energy attenuation, multimodal, dispersion, and on-site noise when the UGW propagates in the rail, the amplitude of the received UGW signal is low and the signal-to-noise ratio is poor. Therefore, this thesis mainly systematically studies the characteristics optimization of PUTs from the aspects of impedance matching, driving circuit optimization, and excitation signal optimization. The main work is as follows: 1. To deeply study of the electromechanical characteristics of longitudinal vibration sandwich piezoelectric ultrasonic transducer (referred to as PUTs), the PSpice equivalent circuit models of a piezoelectric ultrasonic transducer and the PSpice equivalent circuit model of a pitch-catch setup are established based on one-dimensional wave and transmission line theory. The PSpice model of the PUT and the PSpice model of the pitch-catch setup are analyzed from the time and frequency domains, respectively, and the accuracy of the built PSpice models is verified through some experiments. It is shown that the PSpice model of a PUT established above is highly scalable and can be combined with amplifiers, driving circuits, diodes. 2. With the aim of solving the problem of impedance mismatch between the piezoelectric ultrasonic transducer and the driving circuit and the rail surface, the effect of the impedance matching on the electromechanical properties of the piezoelectric ultrasonic transducer was studied from the electrical and acoustic ends, respectively. From the electrical side, the effects of different electrical impedance matching networks on the electromechanical characteristics of PUTs are studied in both time and frequency domains. It is shown that in the two LC impedance matching networks, the matching network formed by the series inductance and parallel capacitance is better. From the acoustic side, an experimental method is used to study the effect of acoustic impedance matching on the transient characteristics of PUTs. It is concluded that when the epoxy resin is doped with 10% tungsten powder and the coating thickness is 8 mm, the acoustic impedance matching effect is better. 3. To overcome the problems of the existing driving circuits that the excitation voltage is not high enough, the extra high voltage DC voltage is required and the impedance matching is not considered, this thesis proposed a high voltage pulse driving circuit based on the full-bridge topology. The driving circuit takes into account the suppression of overshoot and oscillation when the power MOSFET is turned off, and at the same time conducts the impedance matching and tailing absorption of the excitation signal for PUTs. The suppression of overshoot and oscillation adopts the RC snubber circuit, and the tailing absorption is accomplished by a bleeder resistor and a bidirectional thyristor. The correctness and effectiveness of the proposed high-voltage pulse driving circuit are verified through experiments. It was also found that the combined use of electrical impedance matching and absorption circuits can effectively improve the energy conversion efficiency of PUTs. 4. To obtain the optimal performance of PUTs, the excitation signal of PUTs is optimized in terms of excitation signal frequency and excitation coding. First of all, to solve the problem of PUTs with having a resonance frequency shift after loading, this thesis proposes an optimal excitation frequency tracking method based on a digital band-pass tracking filtering. Then its correctness and stability are verified through some field experiments. Secondly, to improve the signal-to-noise ratio of the UGW signal, it is proposed to apply the Barker code excitation method to the broken rail detection, and use the pulse compression technique at the receiving end to realize the rapid recognition of the signal characteristics. Finally, for the case where the pulse-compressed signal produces undesirable peak sidelobes due to the effects of bandwidth, multipath, and noise, an adaptive peak detection algorithm based on the Hilbert transform combined with a digital bandpass tracking filter and a triangle filter. The accuracy and effectiveness of the above-mentioned Barker code excitation method and the adaptive peak detection algorithm are verified through experiments. The study in this thesis presents a feasible solution for improving the propagation efficiency of UGW in the rails and at the same time provides theoretical guidance for the large-scale application of the real-time broken rail detection system based on UGW