Advanced monitoring of rail breakage in double-track railway lines by means of PCA techniques

Espinosa, Felipe; Garcı́a, J.J.; Hernández, Álvaro; Mazo, Manuel; Ureña, J.; Jiménez, Javier; Fernández, Ignacio; Perez, Carlos L.; García, Juan Carlos García

doi:10.1016/j.asoc.2017.11.009

Cited by 11 publications

(4 citation statements)

References 28 publications

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“…Some of the identified algorithms for these industry 4.0 technologies are the following: Breadth First Search (BFS) algorithm or Genetic Algorithms (GA) for DAS systems [ 53 , 55 ], Principal Component Analysis (PCA) algorithm for monitoring rail breakage [ 18 ], Artificial Bee Colony (ABC) algorithm for a train traction control systems [ 57 ], Dynamic Differential Evolution (RHMDE) algorithm for tracking the rail state [ 10 ], fuzzy systems or deep-learning models for rail maintenance [ 28 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Laiton-Bonadiez

Bedoya

Zapata-Cortés

et al. 2022

Sensors

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Background: Industry 4.0 technologies have been widely used in the railway industry, focusing mainly on maintenance and control tasks necessary in the railway infrastructure. Given the great potential that these technologies offer, the scientific community has come to use them in varied ways to solve a wide range of problems such as train failures, train station security, rail system control and communication in hard-to-reach areas, among others. For this reason, this paper aims to answer the following research questions: what are the main issues in the railway transport industry, what are the technologic strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 that are used in the railway transport industry to solve the aforementioned issues? Methods: This study adopts a systematic literature review approach. We searched the Science Direct and Web of Science database inception from January 2017 to November 2021. Studies published in conferences or journals written in English or Spanish were included for initial process evaluation. The initial included papers were analyzed by authors and selected based on whether they helped answer the proposed research questions or not. Results: Of the recovered 515 articles, 109 were eligible, from which we could identify three main application domains in the railway industry: monitoring, decision and planification techniques, and communication and security. Regarding industry 4.0 technologies, we identified 9 different technologies applied in reviewed studies: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). This study is, to our knowledge, one of the first to show how industry 4.0 technologies are currently being used to tackle railway industry problems and current application trends in the scientific community, which is highly useful for the development of future studies and more advanced solutions. Funding: Colombian national organizations Minciencias and the Mining-Energy Planning Unit.

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Section: Resultsmentioning

confidence: 99%

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Laiton-Bonadiez

Bedoya

Zapata-Cortés

et al. 2022

Sensors

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“…Generally, PCA is sub-divided into two phases [41]. The first, also called the training phase, is performed offline and works with a set of previous measurement vectors containing the information corresponding to a given class or pattern, in this case, from the driving profile of first driver (D1) (Table 1), which is labelled based on (9) as the pattern driving profile.…”

Section: Classifier Based On Hotelling Transform (Ht)mentioning

confidence: 99%

Driving Behaviour Estimation System Considering the Effect of Road Geometry by Means of Deep NN and Hotelling Transform

Barreno,

Santos,

Romana

2024

Electronics

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In this work, an intelligent hybrid model is proposed to identify hazardous or inattentive driving manoeuvres on roads, with the final goal being to increase and ensure travellers’ safety and comfort. The estimation is based on the effects that road geometry may have on vehicle accelerations, displacements and dynamics. The outputs of the intelligent systems proposed are how the type of driving can be characterized as normal, careless or distracted. The intelligent system consists of an LSTM (Long Short-Term Memory) neural network in a first step that distinguishes between normal and abnormal driving behaviour and then a second module that classifies abnormal forms of driving as aggressive or inattentive, with the latter implemented with another LSTM, a CNN (convolutional neural network) or the Hotelling transform. They are applied to some of the characteristics of vehicle dynamics to estimate the driving behaviour. Smartphone inertial sensors such as GPS, accelerometers and gyroscopes are used to measure these vehicle characteristics and to identify driving events in manoeuvres. Specifically, the critical acceleration due to the influence of the road geometry can be measured with inertial sensors, and then, this road acceleration with the lateral acceleration allows us to estimate the driver’s perceived acceleration. This perceived acceleration affects the driving style and, consequently, the estimation of the appropriate speed to travel on that road. There is use of both a traditional two-lane and a motorway route located in the Madrid region of Spain. Driving behaviour is determined by considering how changes in road geometry may affect one’s driving style and, consequently, the estimation of the proper speed. The results obtained with some of the proposed configurations of the intelligent hybrid system reach an accuracy of 97.21% in detecting dangerous driving or driving with a certain risk. This could allow generating real-time alerts for potentially dangerous or inattentive manoeuvres, leading to safer and more appropriate driving.

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“…Studies have proposed effective variable reduction techniques that involve diverse PCA-based approaches for processing sensed data with multiple variables. Using double-track railway lines as the detection objects, Espinosa et al [7] used PCA-based classification for identifying broken rails; their derived experimental results demonstrated a 100% success rate. Li et al [8] applied PCA in a nuclear power plant to detect faults and reconstruct sensors.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

Lin

2019

Journal of Sensors

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This paper developed a principal component analysis (PCA)-integrated algorithm for feature identification in manufacturing; this algorithm is based on an adaptive PCA-based scheme for identifying image features in vision-based inspection. PCA is a commonly used statistical method for pattern recognition tasks, but an effective PCA-based approach for identifying suitable image features in manufacturing has yet to be developed. Unsuitable image features tend to yield poor results when used in conventional visual inspections. Furthermore, research has revealed that the use of unsuitable or redundant features might influence the performance of object detection. To address these problems, the adaptive PCA-based algorithm developed in this study entails the identification of suitable image features using a support vector machine (SVM) model for inspecting of various object images; this approach can be used for solving the inherent problem of detection that occurs when the extraction contains challenging image features in manufacturing processes. The results of experiments indicated that the proposed algorithm can successfully be used to adaptively select appropriate image features. The algorithm combines image feature extraction and PCA/SVM classification to detect patterns in manufacturing. The algorithm was determined to achieve high-performance detection and to outperform the existing methods.

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Advanced monitoring of rail breakage in double-track railway lines by means of PCA techniques

Cited by 11 publications

References 28 publications

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Driving Behaviour Estimation System Considering the Effect of Road Geometry by Means of Deep NN and Hotelling Transform

Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

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