Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains

Ren, Yifei; O’Brien, Eugene J.; Cantero, Daniel; Keenahan, Jennifer

doi:10.3390/app12104972

Cited by 12 publications

(4 citation statements)

References 43 publications

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“…As a comparison, Figure 11 also shows the obtained fltered roughness profle when integrating twice the OBM acceleration data. Tis is a common approach to obtain the roughness profles of rails [9,10] that is characterized by simplicity and ease of implementation. Although double integration returns acceptable results, the profle estimated via DKF after model updating shows a better agreement with the roughness profle measured by the laser scanners (considered here the ground truth).…”

Section: Application On Field Obm Data From An Sbb Diagnosticmentioning

confidence: 99%

“…Likely, the most straightforward method to determine rail roughness profles based on vibration data is yielded via double integration of acceleration measurements from diferent vehicle parts [9,10]. However, integrating the measured signals often leads to low accuracy due to integration errors relating to noise content and associated drifts at low frequencies.…”

Section: Introductionmentioning

confidence: 99%

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A Model-Based Bayesian Inference Approach for On-Board Monitoring of Rail Roughness Profiles: Application on Field Measurement Data of the Swiss Federal Railways Network

Stoura,

Dertimanis,

Hoelzl

et al. 2023

Structural Control and Health Monitoring

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According to the International Union of Railways, railway networks count more than one million kilometers of tracks worldwide, a number that is to rise further as the goal is to promote rail transportation as a sustainable means to face the challenge of increased mobility. However, such a vast expansion further necessitates efficient and reliable infrastructure monitoring schemes able to guarantee the quality and safety of rail transportation. Traditional monitoring approaches, relying on visual inspection and portable measuring devices, cannot rise to the task as they do not allow for continuous inspection of extended portions of rail infrastructure. Therefore, mobile monitoring methodologies based on dedicated diagnostic vehicles have emerged as an alternative. Despite revolutionizing traditional monitoring methods, such vehicles are usually expensive and can only operate under the suspension of regular rail service. In this work, we propose an alternative approach for mobile sensing of railway infrastructure based on on-board monitoring data collected from low-cost vibration sensors, e.g., accelerometers, which can be mounted on in-service trains. Specifically, we focus on identifying the roughness profile of the tracks and propose a fusion of reduced-order vehicle models with a Bayesian inference approach for joint input-state estimation. To enhance the inference, we opt for a prior updating of the vehicle model parameters on the basis of an unscented Kalman filter and available measurements from a diagnostic vehicle. The key contributions of this work are (i) the consideration of the dynamic interaction between trains and tracks, which is usually ignored in rail roughness estimation, (ii) the adoption of reduced train vehicle models that decrease the computational effort of the identification task, (iii) the updating of the vehicle parameters to account for inconsistencies in the model used, and (iv) the application of the proposed methodology to actual acceleration measurements collected from a diagnostic vehicle of the Swiss Federal Railways network.

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Section: Application On Field Obm Data From An Sbb Diagnosticmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Model-Based Bayesian Inference Approach for On-Board Monitoring of Rail Roughness Profiles: Application on Field Measurement Data of the Swiss Federal Railways Network

Stoura,

Dertimanis,

Hoelzl

et al. 2023

Structural Control and Health Monitoring

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“…A novel method to determine apparent profile (AP) of the track and detect railway bridge condition using sensors on in-service trains was proposed by Ren et al [75]. An optimization algorithm was used to find vehicle dynamic properties and speed, and the apparent profile was used to calculate the moving reference deflection influence line.…”

Section: Feature Extractionmentioning

confidence: 99%

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part I: Bridges and Viaducts

et al. 2023

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Bridges and viaducts are critical components of railway transport infrastructures, providing safe and efficient means for trains to cross over natural barriers such as rivers and valleys. Ensuring the continuous safe operation of these structures is therefore essential to avoid disastrous economic consequences and even human losses. Drive-by methodologies have emerged as a potential and cost-effective monitoring solution for accurately and prematurely detecting damage based on instrumented vehicles while minimizing disruptions to train operations. This paper presents a critical review of drive-by methodologies applied to bridges and viaducts. Firstly, the premises of the method are briefly reviewed, and the potential applications are discussed. In sequence, several works involving the use of drive-by methodologies for modal characteristic extraction are presented, encompassing the most important methodologies developed over time as well as recent advancements in the field. Finally, the problem of damage identification is discussed—both in relation to modal and non-modal parameter-based techniques considering the most promising features and the current advancements in the development of methodologies for damage detection based on machine learning algorithms. A comprehensive conclusion is presented at the end of the article, summarizing the achievements and providing perspectives for future developments. By critically assessing the application of drive-by methodologies to bridges and viaducts, this paper contributes to the advancement of knowledge in this crucial area, emphasizing the significance of continuous monitoring for ensuring the integrity and safety of these vital transport infrastructures.

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“…This method might not be suitable for dedicated measuring an S&C [10]. It is both expensive and could even cause disruption to scheduled services [11]. Other non-destructive testing (NDT) techniques adopted to evaluate the rail defects include the vision-based techniques, ultrasound measurements, eddy current testing (ECT) systems, accelerometers, etc.…”

Section: Introductionmentioning

confidence: 99%

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

et al. 2023

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Switches and crossings (S&Cs) are also known as turnouts or railway points. They are important assets in railway infrastructures and a defect in such a critical asset might lead to a long delay for the railway network and decrease the quality of service. A squat is a common rail head defect for S&Cs and needs to be detected and monitored as early as possible to avoid costly emergent maintenance activities and enhance both the reliability and availability of the railway system. Squats on the switchblade could even potentially cause the blade to break and cause a derailment. This study presented a method to collect and process vibration data at the point machine with accelerometers on three axes to extract useful features. The two most important features, the number of peaks and the total power, were found. Three different unsupervised machine learning algorithms were applied to cluster the data. The results showed that the presented method could provide promising features. The k-means and the agglomerative hierarchical clustering methods are suitable for this data set. The density-based spatial clustering of applications with noise (DBSCAN) encounters some challenges.

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Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains

Cited by 12 publications

References 43 publications

A Model-Based Bayesian Inference Approach for On-Board Monitoring of Rail Roughness Profiles: Application on Field Measurement Data of the Swiss Federal Railways Network

A Model-Based Bayesian Inference Approach for On-Board Monitoring of Rail Roughness Profiles: Application on Field Measurement Data of the Swiss Federal Railways Network

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part I: Bridges and Viaducts

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

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