Prediction of tram track gauge deviation using artificial neural network and support vector regression

Falamarzi, Amir; Moridpour, Sara; Nazem, Majidreza; Cheraghi, Samira

doi:10.1080/14488353.2019.1616357

Cited by 21 publications

(16 citation statements)

References 18 publications

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“…ANN and SVM ML algorithms are applied in developing gauge degradation measurements prediction for two types of rail track including straight and curved segments by Falamarzi, A. et al [48]. Mean squared error and coefficient of determination are used in the performance evaluation of the proposed models, ANN with greater than 0.9 coefficient of determination value.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…In the PdM of industrial equipment, SVMs have been widely applied for identifying a specific status based on the acquired signal [55]. SVM and ANN ML algorithms are applied in developing gauge degradation measurements prediction for two types of rail track including straight and curved segments by Falamarzi, A. et al [48], where mean squared error and coefficient of determination are used in the performance evaluation of the proposed models, SVM with greater than 0.75 coefficient of determination value. Based on the results obtained from the study, both ANN and SVM models provide satisfactory and slightly similar outcomes, but, the performance of SVM models in predicting gauge deviation of curved segments is slightly better than ANN models.…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

“…They have been successfully applied in several field of PdM applications. Some authors [10,30,[46][47][48][75][76][77]107,114,[118][119][120][121] focused on ANN ML algorithms. Some other authors [64][65][66][67]81,82,[84][85][86][87][88]90,116,117] studied RF technique.…”

mentioning

confidence: 99%

“…Some other authors [64][65][66][67]81,82,[84][85][86][87][88]90,116,117] studied RF technique. In the last years, use of SVM technique has received attention from authors [14,21,48,56,66,79,[93][94][95][96][97][98][99][100]102,107,111,115,121,124]. Some authors [21,56,82,112,120,124] have given attention to GBM technique.…”

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confidence: 99%

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Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Decision Tree (Dt)mentioning

confidence: 99%

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confidence: 99%

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Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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“…Neural network is an active borderline interdisciplinary. e artificial neural network is relative to the biological neural network system in biology [6]. Its purpose is to use a certain simple mathematical model to describe the structure of the biological neural network.…”

Section: Introductionmentioning

confidence: 99%

Tracing Mechanism of Sports Competition Pressure Based on Backpropagation Neural Network

Zhao

Liu

2021

Complexity

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Through the overall situation of athletes’ competition pressure, the pressure level of participating athletes can be understood and revealed. Analyzing the sources of stress and influencing factors of athletes can find measures to relieve and reduce stress and provide theoretical reference for the regulation of athletes’ competition pressure. Based on genetic algorithm and neural network theory, this paper proposes a method of tracing the sports competition pressure based on genetic algorithm backpropagation (BP) neural network to solve the problem that traditional neural network learning algorithm is slow and easy to fall into local minimum. There is no significant difference between male and female athletes in the level of competition pressure. Athletes have the same training methods and the same goals, and the competition pressure tends to be the same, with no obvious difference; athletes with different educational backgrounds have no significant differences in training, academics, sports injuries, interpersonal relationships, social expectations, and evaluations. Due to the particularity of the stage, the competition pressure of fourth-year undergraduate and third-year masters is significantly higher than that of other grades. The number of athletes participating in college table tennis tournaments has very significant differences in competition dimensions. There is significant difference in training and self-expectation dimension difference. The competition pressure of athletes who participated in the college table tennis championship for the first time was significantly higher than that of athletes who participated repeatedly. There were significant differences between athletes before and after adapting to the venue. Before adapting to the venue, the competition pressure of athletes is generally greater. After adapting to the venue, the competition pressure of athletes has been relieved.

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A Three-Stage Prediction Method for Track Displacement During Shield Tunneling

et al. 2023

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Track displacement is an important factor of track irregularity. Existing researches related with track displacement prediction generally ignore the influence from underground construction engineering such as shield tunneling, resulting in inaccurate estimation of track displacement. To fill this gap, we propose a three-stage framework to predict the track displacement when the shield tunnel under crosses the existing tunnel. Firstly, by considering the curved shield tunneling, a three-dimensional model is constructed to estimate the total ground displacement during the whole tunneling process. Furthermore, the soil-tunnel interaction model is established to estimate the deformation of the existing tunnel. To tackle the issue of unknown node displacements, cubic splines are used to interpolate the unknown values of tunnel displacements. On this basis, the direct stiffness method is used to estimate the track displacement and to calculate the track irregularity. Finally, the effectiveness of the proposed method is verified and the prediction performance on the track irregularity is evaluated using a real engineering case and the finite element simulation. The main contributions of this article lie in the modeling of the curved scenario for the estimation of the ground loss, as well as the combination of cubic splines and direct stiffness method, which improve the accuracy of the track displacement estimation during shield tunneling.

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Prediction of tram track gauge deviation using artificial neural network and support vector regression

Cited by 21 publications

References 18 publications

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Tracing Mechanism of Sports Competition Pressure Based on Backpropagation Neural Network

A Three-Stage Prediction Method for Track Displacement During Shield Tunneling

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