Deep Learning for Track Quality Evaluation of High-Speed Railway Based on Vehicle-Body Vibration Prediction

Ma, Shuai; Gao, Liang; Liu, Xiubo; Lin, Jing

doi:10.1109/access.2019.2960537

Cited by 58 publications

(28 citation statements)

References 23 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LSTM is a distinct version of RNN which deals with the vanishing gradient problem, which considers the time notion, and which solves the problem of storing short-term data over long periods of time. The LSTM architecture is more appropriate for the temporal modelling of sequence data [25]. The main idea behind the LSTM concept is the memory block that memorizes its state over the training process.…”

Section: Long Short-term Memory (Lstm) For Feature Learningmentioning

confidence: 99%

Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

Najeh

Lundberg

Kerrouche

2021

Sensors

View full text Add to dashboard Cite

The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.

show abstract

Section: Long Short-term Memory (Lstm) For Feature Learningmentioning

confidence: 99%

Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

Najeh

Lundberg

Kerrouche

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…This specific structure contributes to a longer period of remembering ability. Ma [53] used an LSTM model to predict vehicle-body vibration. A CNN was used to extract features and the LSTM was utilized to find the inherent pattern in the track geometry time-series.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

et al. 2020

View full text Add to dashboard Cite

Conventional planning of maintenance and renewal work for railway track is based on heuristics and simple scheduling. The railway industry is now collecting a large amount of data with the fast-paced development of sensor technologies. These data sets carry information about the conditions of various components in railway track. Since just before the beginning of the 21st century, data-driven models have been used in the predictive maintenance of railway track. This study presents a systematic literature review of data-driven models applied in the predictive maintenance of railway track. A taxonomy to classify the existing literature based on types of models and types of applications is provided. It is found that applying the deep learning methods, unsupervised methods, and ensemble methods are the new trends for predictive maintenance of railway track. Rail geometry irregularity, rail head defect, and missing rail components detection were the top three most commonly considered issues within the application of data-driven models. Prediction of rail breaks has received increasing attention in the last four years. Among these data-driven model applications, the collected data types are the most critical factors which affect selecting suitable models. Finally, this study discusses upcoming challenges in the predictive maintenance of railway track.

show abstract

“…Similarly, computer-based image recognition has been applied to detect and recognise railway infrastructure and changes in the surrounding environment [25]. Deep learning techniques have also been proposed to evaluate rail quality using track geometry [26]. Such research focuses on how to utilise technology such as the convolutional neural network (CNN) to analyse big data collected by railway systems to build riskrecognition frameworks-in the case of FSTs-risk in the railway stations, Fig.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

View full text Add to dashboard Cite

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

show abstract

Deep Learning for Track Quality Evaluation of High-Speed Railway Based on Vehicle-Body Vibration Prediction

Cited by 58 publications

References 23 publications

Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

A Deep Learning Approach Towards Railway Safety Risk Assessment

Contact Info

Product

Resources

About