FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision (original) (raw)
Related papers
Improving Railway Safety with AI: Detecting Defective Fasteners using Deep Learning
Zenodo (CERN European Organization for Nuclear Research), 2023
Railway track inspection is a vital task to ensure safe and efficient train travel. However, traditional inspection methods require people to walk along the tracks and look for issues, which is time-consuming and exhausting. To address this, we created a new system that uses a camera installed under a train to take pictures of the tracks automatically. Then, we applied advanced computer methods like YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and ResNet50 (Residual Network50) to analyze the images and identify any areas that need fixing. ResNet50 is a type of computer network that can recognize patterns in pictures, and we used it to enhance the performance of the other two methods. Our system achieved an average detection rate of 99%, meaning that it can identify track anomalies as accurately as a human operator but much faster. This new system has several benefits over traditional methods, such as increasing efficiency and accuracy, minimizing human involvement, and reducing operational costs. By improving safety and reducing the risk of accidents, this technology has the potential to revolutionize the railway maintenance industry. In summary, our study presents a new automated technique for detecting rail anomalies using computer vision and deep learning-based methods. This technology can help make train travel safer and more efficient, ultimately benefiting both passengers and railway companies.
Turkish Journal of Science and Technology, 2022
A means of transportation is the way in which an object, person, or service is transported from one place to another. Rail transportation occupies an important place in terms of cost and reliability. Most train accidents are caused by faults in railroad tracks. Detecting faults in railroad tracks is a difficult and time-consuming process compared to conventional methods. In this study, an artificial intelligence based model is proposed that can detect faults in railroad tracks. The dataset used in the study consists of defective and non-defective railroad images. The proposed model consists of foldable neural networks developed using the Tensorflow library. Softmax method was used as a classifier. An overall accuracy of 92.21% was achieved in the experiment.
Fault Diagnosis in Railway Track using Efficient Net based CNN
Ymer, 2022
Railway transportation is the most cost-effective and convenient means of passenger travel in India. The cress cross tracks are almost present in every part of India. Keeping in mind the security of people also the free running railway without any problem, we have to focus in the safety part of this system [1]. In India, the railway network accounts for over 80% of all transportation. Approximately 60% of accidents occur at crossings of railway tracks because of a railway track fracture, resulting in the loss of valuable life and economic damage. As a result, new technology is required for both fault detection and object detection in railway tracks. This technology must be resilient, efficient, and steady [2]. This paper presents a vision based method to find some common defects in railways. Some images have been collected of railway track and image processing method is used to preprocess these images and to detect the features related to defective parts. An EfficientNet based CNN model is developed to detect the defects which uses global average pooling, adam as optimizer, softmax as activation function and categorical_crossentropy as loss function. This research result consists of a classification report as defective and non-defective parts or image with accuracy of 91 percentages over 30 epochs.
Railway Infrastructure Defects Recognition using Fine-grained Deep Convolutional Neural Networks
2018 Digital Image Computing: Techniques and Applications (DICTA), 2018
Railway power supply infrastructure is one of the most important components of railway transportation. As the key step of railway maintenance system, power supply infrastructure defects recognition plays a vital role in the whole defects inspection subsystem. Traditional defects recognition task is performed manually, which is time-consuming and high-labor costing. Inspired by the great success of deep neural networks in dealing with different vision tasks, this paper presents an end-to-end deep network to solve the railway infrastructure defects detection problem. More importantly, this paper is the first work that adopts the idea of deep fine-grained classification to do railway defects detection. We propose a new bilinear deep network named Spatial Transformer And Bilinear Low-Rank (STABLR) model and apply it to railway infrastructure defects detection. The experimental results demonstrate that the proposed method outperforms both hand-craft features based machine learning methods and classic deep neural network methods.
Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning
Applied Sciences
Railway wheelsets are the key to ensuring the safe operation of trains. To achieve zero-defect production, railway equipment manufacturers must strictly control every link in the wheelset production process. The press-fit curve output by the wheelset assembly machine is an essential indicator of the wheelset’s assembly quality. The operators will still need to manually and individually recheck press-fit curves in our practical case. However, there are many uncertainties in the manual inspection. For example, subjective judgment can easily cause inconsistent judgment results between different inspectors, or the probability of human misinterpretation can increase as the working hours increase. Therefore, this study proposes an intelligent railway wheelset inspection system based on deep learning, which improves the reliability and efficiency of manual inspection of wheelset assembly quality. To solve the severe imbalance in the number of collected images, this study establishes a pred...
Railroad Surface Defect Segmentation Using a Modified Fully Convolutional Network
KSII Transactions on Internet and Information Systems, 2020
This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.
A Review on Deep Learning Techniques for Railway Infrastructure Monitoring
IEEE Access
In the last decade, thanks to a widespread diffusion of powerful computing machines, artificial intelligence has been attracting the attention of the academic and industrial worlds. This review aims to understand how the scientific community is approaching the use of deep-learning techniques in a particular industrial sector, the railway. This work is an in-depth analysis related to the last years of the way this new technology can try to provide answers even in a field where the primary requirement is to improve the already very high levels of safety. A strategic and constantly evolving field such as the railway sector could not remain extraneous to the use of this new and promising technology. Deep learning algorithms devoted to the classification, segmentation, and detection of the faults that affect the railway area and the overhead contact system are discussed. The railway sector offers many aspects that can be investigated with these techniques. This work aims to expose the possible applications of deep learning in the railway sector established on the type of recovered information and the type of algorithms to be used accordingly.
2019
Intelligent fault detection by sensor data can ensure the reliability and availability of critical infrastructures. Switches and crossings (S&C) are one of the most important assets of railway networks. They divert trains in different directions by shifting the position of switch rail by point operating equipment (POE). The sensors record the electrical current drawn by the motor in POE. The extraction of features from time-series sensor data enables the detection of faults in POE. This paper proposes a deep learning model to detect faults in railway POE without the need for preprocessing the raw time-series data. It is based on 1-D convolution neural network. The architecture of the proposed deep learning network consists of three types of layers. The first layer is called the local convolution layer. It consists of three 1-D convolution layer to extract local temporal features from three non-overlapping segments of time-series data of different operating phases of POE. The second layer is fully-connected convolution layer. It extracts global temporal features. And the last layer is the output layer, it provides the binary output of fault or fault free for a given sensor data. The result shows that this framework can classify fault with 95.60% accuracy.