Investigation of the effect of the inspection intervals on the track geometry condition

Soleimanmeigouni, Iman; Ahmadi, Alireza; Khajehei, Hamid; Nissen, Arne

doi:10.1080/15732479.2019.1687528

Cited by 14 publications

(10 citation statements)

References 15 publications

(16 reference statements)

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“…Traditionally, TG parameters are measured by the dedicated TG inspection systems in regular inspections, which are typically based on high-value laser triangulation sensors or/and inertial measurement units (IMU). The inspection interval is usually defined as several months, which results in the lack of up-to-date information on track conditions [11]. In order to improve the information availability and enable efficient maintenance decisions, TG monitoring on in-service vehicles was proposed.…”

Section: Background and Motivationmentioning

confidence: 99%

Deep learning based virtual point tracking for real-time target-less dynamic displacement measurement in railway applications

Shi

Šabanovič

Rizzetto

et al. 2022

Mechanical Systems and Signal Processing

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In the application of computer-vision-based displacement measurement, an optical target is usually required to prove the reference. If the optical target cannot be attached to the measuring objective, edge detection and template matching are the most common approaches in target-less photogrammetry. However, their performance significantly relies on parameter settings. This becomes problematic in dynamic scenes where complicated background texture exists and varies over time. We propose virtual point tracking for real-time target-less dynamic displacement measurement, incorporating deep learning techniques and domain knowledge to tackle this issue. Our approach consists of three steps: 1) automatic calibration for detection of region of interest; 2) virtual point detection for each video frame using deep convolutional neural network; 3) domain-knowledge based rule engine for point tracking in adjacent frames. The proposed approach can be executed on an edge computer in a real-time manner (i.e. over 30 frames per second). We demonstrate our approach for a railway application, where the lateral displacement of the wheel on the rail is measured during operation. The numerical experiments have been performed to evaluate our approach's performance and latency in a harsh railway environment with dynamic complex backgrounds. We make our code and data available at https://github. com/quickhdsdc/Point-Tracking-for-Displacement-Measurement-in-Railway-Applications.

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Section: Background and Motivationmentioning

confidence: 99%

Deep learning based virtual point tracking for real-time target-less dynamic displacement measurement in railway applications

Shi

Šabanovič

Rizzetto

et al. 2022

Mechanical Systems and Signal Processing

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“…When the degradation path exceeding the threshold is deemed as a defect, track condition can be classified as normal and defected states, and logistic regression by its nature is suitable in solving this problem, cf. Andrade and Teixeira (2014), Nissen (2019), Sharma, Cui, He, Mohammadi, and and Soleimanmeigouni et al (2019). Survival analysis is widely used to model the uncertainty in system lifetimes.…”

Section: Statistical Approachmentioning

confidence: 99%

Systems thinking approach for improving maintenance management of discrete rail assets: a review and future perspectives

Shang

Macho

Wang

et al. 2021

Structure and Infrastructure Engineering

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Performance evaluation and maintenance planning are gaining importance with ageing rail infrastructure and increasing demand on track safety and continuous availability. The discrete/point railway assets (e.g. bridges, level crossings) together with extended track sections constitute the main railway network infrastructure. The former has important implications in train safety, riding comfort and operating expenditures due to local intensified degradation and plays a role in effective network capacity due to their large quantity. The heterogeneity in asset features and operating environment also adds difficulties to efficient maintenance planning of multiple discrete assets. The current review screens the issue to level crossings, as little concern has been engaged to this asset type, and draws together different perspectives related to their maintenance management. The systems thinking approach is integrated and two levels of asset management (i.e. micro-and macro-level) are used to structure the synthesis, which are interdependent and synergistic. Two major approaches, namely, the mechanistic and data-driven modelling are synthesised. Both contribute to the maintenance knowledge and their comparisons are elaborated. Limitations in existing studies are identified and directions for future research are provided, aiming to contribute to a more refined 'inspection and diagnosis' process to properly capture the local track issues and move towards system-level maintenance approach for multiple level crossings.

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“…In order to consider the UH2 defects in the optimization model, it is necessary to predict the probability of the occurrence of UH2 defects in track section sS in maintenance window t, i.e., P s, t : It has been shown that the probability of the occurrence of severe isolated defects is dependent on the typical track geometry indicators used to plan maintenance activities, e.g. the SDLL (Andrade & Teixeira, 2014;Soleimanmeigouni, Ahmadi, Khajehei & Nissen, 2020).…”

Section: Modelling the Probability Of Uh2 Defectsmentioning

confidence: 99%

Optimal opportunistic tamping scheduling for railway track geometry

Khajehei

Haddadzade

Ahmadi

et al. 2020

Structure and Infrastructure Engineering

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This study has been dedicated to the optimization of opportunistic tamping scheduling. The aim of this study has been to schedule tamping activities in such a way that the total maintenance costs and the number of unplanned tamping activities are minimized. To achieve this, the track geometry tamping scheduling problem was defined and formulated as a mixed integer linear programming (MILP) model and a genetic algorithm was used to solve the problem. Both the standard deviation of the longitudinal level and the extreme values of isolated defects were used to characterize the track geometry quality and to plan maintenance activities. The performance of the proposed model was tested on data collected from the Main Western Line in Sweden. The results show that different scenarios for controlling and managing isolated defects will result in optimal scheduling plan. It is also found that to achieve more realistic results, the speed of the tamping machine and the unused life of the track sections should be considered in the model. Moreover, the results show that prediction of geometry condition without considering the destructive effect of tamping will lead to an underestimation of the maintenance needs by 2%.

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Investigation of the effect of the inspection intervals on the track geometry condition

Cited by 14 publications

References 15 publications

Deep learning based virtual point tracking for real-time target-less dynamic displacement measurement in railway applications

Deep learning based virtual point tracking for real-time target-less dynamic displacement measurement in railway applications

Systems thinking approach for improving maintenance management of discrete rail assets: a review and future perspectives

Optimal opportunistic tamping scheduling for railway track geometry

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