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Rantatalo, M., Chandran, P., Thiery, F., Odelius, J., Gustafsson, C., Asplund, M. & Kumar, U. (2023). Evaluation of Measurement Strategy for Track Side Monitoring of Railway Wheels. Applied Sciences, 13(9), Article ID 5382.
Open this publication in new window or tab >>Evaluation of Measurement Strategy for Track Side Monitoring of Railway Wheels
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2023 (English)In: Applied Sciences, ISSN 2076-3417, Vol. 13, no 9, article id 5382Article in journal (Refereed) Published
Abstract [en]

Wheelsets form an indispensable part of the railway rolling stock and need to be periodically inspected to ensure stable, safe, reliable, and sustainable rail operation. Wheel profiles are usually inspected and measured in a workshop environment using handheld equipment or by utilizing wayside measuring equipment. A common practice for both methods is to measure the wheel profile at one position along the circumference of the wheel, resulting in a one-slice measurement strategy, based on the assumption that the wheel profile has the same shape independent of the measurement position along the wheel. In this article, the representability of a one-slice measurement strategy with respect to the wheel profile parameters is investigated using handheld measurement equipment. The calculated range of standard deviation of the parameters estimated such as flange height, flange width, flange slope, and hollow wear from the measurements shows a spread in the parameter value along the circumference of the wheel. As an initial validation of the results, measurements from the wayside monitoring systems were also investigated to see if a similar spread was visible. The spread was significantly higher for flange height, flange width, and flange slope estimated from wayside measurement equipment than for the same parameters estimated using the handheld measurement equipment.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
railway wheel, wheel profile, wheel parameters, wayside monitoring, condition monitoring
National Category
Other Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-97646 (URN)10.3390/app13095382 (DOI)000987233600001 ()2-s2.0-85159355820 (Scopus ID)
Projects
InfraSweden2030, supported by Vinnova, Formas, and EnergimyndighetenShift2Rail project IN2SMART
Funder
Luleå Railway Research Centre (JVTC)Swedish Transport Administration
Note

Validerad;2023;Nivå 2;2023-05-29 (joosat);

Licens fulltext: CC BY License

Available from: 2023-05-29 Created: 2023-05-29 Last updated: 2023-09-05Bibliographically approved
Zuo, Y., Lundberg, J., Chandran, P. & Rantatalo, M. (2023). Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning. Applied Sciences, 13(9), Article ID 5376.
Open this publication in new window or tab >>Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning
2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 9, article id 5376Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2023
National Category
Other Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-96996 (URN)10.3390/app13095376 (DOI)2-s2.0-85159369539 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-05-03 (joosat);

Licens fulltext: CC BY License

Available from: 2023-05-03 Created: 2023-05-03 Last updated: 2023-10-11Bibliographically approved
Zuo, Y., Lundberg, J., Najeh, T., Rantatalo, M. & Odelius, J. (2023). Squat Detection of Railway Switches and Crossings Using Point Machine Vibration Measurements. Sensors, 23(7), Article ID 3666.
Open this publication in new window or tab >>Squat Detection of Railway Switches and Crossings Using Point Machine Vibration Measurements
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2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 7, article id 3666Article in journal (Refereed) Published
Abstract [en]

Railway switches and crossings (S&C) are among the most important high-value components in a railway network and a failure of such an asset could result in severe network disturbance. Therefore, potential defects need to be detected at an early stage to prevent traffic-disturbing downtime or even severe accidents. A squat is a common defect of S&Cs that has to be monitored and repaired to reduce such risks. In this study, a testbed including a full-scale S&C and a bogie wagon was developed. Vibrations were measured for different squat sizes by an accelerometer mounted at the point machine. A method of processing the vibration data and the speed data is proposed to investigate the possibility of detecting and quantifying the severity of a squat. One key technology used is wavelet denoising. The study shows that it is possible to monitor the development of the squat size on the rail up to around 13 m from the point machine. The relationships between the normalised peak-to-peak amplitude of the vibration signal and the squat depth were also estimated.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
railway switch & crossing, vibration, squats, condition monitoring, wavelet denoising, fault detection
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-96465 (URN)10.3390/s23073666 (DOI)000970143200001 ()37050726 (PubMedID)2-s2.0-85152309487 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-04-13 (johcin);

Funder: Luleå Railway Research Centre (JVTC)

Available from: 2023-04-13 Created: 2023-04-13 Last updated: 2023-10-11Bibliographically approved
Zuo, Y., Chandran, P., Odelius, J. & Rantatalo, M. (2023). Wayside Railway Switch and Crossing Monitoring Using Isolation Forest Anomaly Scores. Sustainability, 15(20), Article ID 14836.
Open this publication in new window or tab >>Wayside Railway Switch and Crossing Monitoring Using Isolation Forest Anomaly Scores
2023 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 15, no 20, article id 14836Article in journal (Refereed) Published
Abstract [en]

Railway switch and crossing (S&C) systems have complicated moving structures compared with regular rail. They require multiple components that vary in complexity. The complexity of railway S&C, together with the fact that they are discontinuous points of the system, makes them vulnerable to defects such as squats. A squat on the switching rail could potentially cause rail breakage and lead to catastrophic results, such as derailment. In this study, a method based on anomaly scoring was investigated to estimate the status of an S&C system with respect to squat defects. The proposed method was tested in a real environment under controlled measurement sequences. The results show that the methods can differ between an S&C with squats and another one without them.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
railway, anomaly detection, anomaly score, rail squat, squat detection, machine learning, unsupervised learning, isolation forest
National Category
Other Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-101654 (URN)10.3390/su152014836 (DOI)
Note

Validerad;2023;Nivå 2;2023-10-16 (joosat);

CC BY 4.0 License

Available from: 2023-10-14 Created: 2023-10-14 Last updated: 2023-10-24Bibliographically approved
Talebiahooie, E., Thiery, F., Mattsson, H. & Rantatalo, M. (2022). An Experimental Study on Railway Ballast Degradation Under Cyclic Loading. In: Ramin Karim; Alireza Ahmadi; Iman Soleimanmeigouni; Ravdeep Kour; Raj Rao (Ed.), International Congress and Workshop on Industrial AI 2021: . Paper presented at International Congress and Workshop on Industrial AI (IAI 2021), Luleå, Sweden, October 5-7, 2021 (pp. 424-433). Springer, 1
Open this publication in new window or tab >>An Experimental Study on Railway Ballast Degradation Under Cyclic Loading
2022 (English)In: International Congress and Workshop on Industrial AI 2021 / [ed] Ramin Karim; Alireza Ahmadi; Iman Soleimanmeigouni; Ravdeep Kour; Raj Rao, Springer, 2022, Vol. 1, p. 424-433Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2022
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364
National Category
Infrastructure Engineering Geotechnical Engineering Applied Mechanics
Research subject
Operation and Maintenance; Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-89301 (URN)10.1007/978-3-030-93639-6_37 (DOI)000777604600037 ()2-s2.0-85125248370 (Scopus ID)
Conference
International Congress and Workshop on Industrial AI (IAI 2021), Luleå, Sweden, October 5-7, 2021
Note

ISBN för värdpublikation: 978-3-030-93638-9, 978-3-030-93639-6

Available from: 2022-02-16 Created: 2022-02-16 Last updated: 2023-09-05Bibliographically approved
Zuo, Y., Thiery, F., Chandran, P., Odelius, J. & Rantatalo, M. (2022). Squat Detection of Railway Switches and Crossings Using Wavelets and Isolation Forest. Sensors, 22(17), Article ID 6357.
Open this publication in new window or tab >>Squat Detection of Railway Switches and Crossings Using Wavelets and Isolation Forest
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2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 17, article id 6357Article in journal (Refereed) Published
Abstract [en]

Railway switches and crossings (S&Cs) are critical, high-value assets in railway networks. A single failure of such an asset could result in severe network disturbance and considerable economical losses. Squats are common rail surface defects of S&Cs and need to be detected and estimated at an early stage to minimise maintenance costs and increase the reliability of S&Cs. For practicality, installation of wired or wireless sensors along the S&C may not be reliable due to the risk of damages of power and signal cables or sensors. To cope with these issues, this study presents a method for collecting and processing vibration data from an accelerometer installed at the point machine to extract features related to the squat defects of the S&C. An unsupervised anomaly-detection method using the isolation forest algorithm is applied to generate anomaly scores from the features. Important features are ranked and selected. This paper describes the procedure of parameter tuning and presents the achieved anomaly scores. The results show that the proposed method is effective and that the generated anomaly scores indicate the health status of an S&C regarding squat defects.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
railway switch and crossing, vibration, squat, anomaly detection, unsupervised machine learning, anomaly score, point machine
National Category
Transport Systems and Logistics Applied Mechanics
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-93013 (URN)10.3390/s22176357 (DOI)000851829500001 ()36080815 (PubMedID)2-s2.0-85137549502 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-09-13 (joosat);

Available from: 2022-09-13 Created: 2022-09-13 Last updated: 2023-09-28Bibliographically approved
Chandran, P., Thiery, F., Odelius, J., Lind, H. & Rantatalo, M. (2022). Unsupervised Machine Learning for Missing Clamp Detection from an In-Service Train Using Differential Eddy Current Sensor. Sustainability, 14(2), 1035-1035
Open this publication in new window or tab >>Unsupervised Machine Learning for Missing Clamp Detection from an In-Service Train Using Differential Eddy Current Sensor
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2022 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 14, no 2, p. 1035-1035Article in journal (Refereed) Published
Abstract [en]

The rail fastening system plays a crucial role in railway tracks as it ensures operational safety by fixing the rail on to the sleeper. Early detection of rail fastener system defects is crucial to ensure track safety and to enable maintenance optimization. Fastener inspections are normally conducted either manually by trained maintenance personnel or by using automated 2-D visual inspection methods. Such methods have drawbacks when visibility is limited, and they are also found to be expensive in terms of system maintenance cost and track possession time. In a previous study, the authors proposed a train-based differential eddy current sensor system based on the principle of electromagnetic induction for fastener inspection that could overcome the challenges mentioned above. The detection in the previous study was carried out with the aid of a supervised machine learning algorithm. This study reports the finding of a case study, along a heavy haul line in the north of Sweden, using the same eddy current sensor system mounted on an in-service freight train. In this study, unsupervised machine learning models for detecting and analyzing missing clamps in a fastener system were developed. The differential eddy current measurement system was set to use a driving field frequency of 27 kHz. An anomaly detection model combining isolation forest (IF) and connectivity-based outlier factor (COF) was implemented to detect anomalies from fastener inspection measurements. To group the anomalies into meaningful clusters and to detect missing clamps within the fastening system, an unsupervised clustering based on the DBSCAN algorithm was also implemented. The models were verified by measuring a section of the track for which the track conditions were known. The proposed anomaly detection model had a detection accuracy of 96.79% and also exhibited a high score of sensitivity and specificity. The DBSCAN model was successful in clustering missing clamps, both one and two missing clamps, from a fastening system separately.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
rail fastening system, clamps, differential eddy current sensor, anomaly detection, clustering
National Category
Infrastructure Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-88849 (URN)10.3390/su14021035 (DOI)000747820700001 ()2-s2.0-85122957194 (Scopus ID)
Funder
Swedish Transport Administration
Note

Validerad;2022;Nivå 2;2022-01-19 (johcin);

Funder: Luleå Railway Research Centre (JVTC); InfraSweden2030, supported by Vinnova, Formas and Energimyndigheten; Shift2Rail project IN2SMART.

Available from: 2022-01-19 Created: 2022-01-19 Last updated: 2023-09-05Bibliographically approved
Chandran, P., Asber, J., Thiery, F., Odelius, J. & Rantatalo, M. (2021). An Investigation of Railway Fastener Detection Using Image Processing and Augmented Deep Learning. Sustainability, 13(21), Article ID 12051.
Open this publication in new window or tab >>An Investigation of Railway Fastener Detection Using Image Processing and Augmented Deep Learning
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2021 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 13, no 21, article id 12051Article in journal (Refereed) Published
Abstract [en]

The rail fastening system forms an indispensable part of the rail tracks and needs to be periodically inspected to ensure safe, reliable and sustainable rail operations. Automated visual inspection has gained significant importance for fastener inspection in recent years. Position accuracy, robustness, and practical limitations due to the complex environment are some of the major concerns associated with this method. This study investigates the combined use of image processing and deep learning algorithms for detecting missing clamps within a rail fastening system. The images used for this study was acquired during field inspections carried out along the Borlänge-Avesta line in Sweden. The image processing techniques proposed in this study enabled the improvement of the fastener position and removal of redundant information from the fastener images. In addition, image augmentation was carried out to enhance the data set, ensure experimental reliability and replicate practical challenges associated with such visual inspection. Convolutional neural network and ResNet-50 algorithms are used for classification purposes, and both the algorithms achieved over 98% accuracy during training and validation and over 94% accuracy during the test stage. Both the algorithms also maintained a good balance between the precision and recall scores during the test stage. CNN and ResNet-50 algorithms were also tested to analyse their performances when the clamp areas were covered. CNN was able to accurately predict the fastener state up to 70% of clamp area occlusion, and ResNet-50 was able to achieve accurate predictions up to 75% of clamp area occlusion.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
rail fastening system, clamps, image processing, deep learning
National Category
Infrastructure Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-87897 (URN)10.3390/su132112051 (DOI)000718577800001 ()2-s2.0-85118417262 (Scopus ID)
Note

Validerad;2021;Nivå 2;2021-11-15 (beamah)

Available from: 2021-11-15 Created: 2021-11-15 Last updated: 2023-09-08Bibliographically approved
Mishra, M., Martinsson, J., Goebel, K. & Rantatalo, M. (2021). Bearing Life Prediction with Informed Hyperprior Distribution: A Bayesian Hierarchical and Machine Learning Approach. IEEE Access, 9, 157002-157011
Open this publication in new window or tab >>Bearing Life Prediction with Informed Hyperprior Distribution: A Bayesian Hierarchical and Machine Learning Approach
2021 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 157002-157011Article in journal (Refereed) Published
Abstract [en]

A Bayesian hierarchical model (BHM) is developed to predict bearing life using envelope acceleration data in combination with a degradation model and prior knowledge of the bearing rating life. The BHM enables the inference of individual bearings, groups of bearings, or bearings operating under certain conditions. The key benefit of the BHM approach is that the relationships between the bearing model parameters and their prior distributions can be expressed at different hierarchical levels. We begin our analysis using a bearing rating life calculation L10h and an estimate of its associated failure time distribution. Realistic variations to constrain our prior distribution of the failure time are then applied before measurements are available. When data become available, estimates more representative of our specific batch and operating conditions are inferred, both on the individual bearing level and the bearing group level. The proposed prognostics methodology can be used in situations with varying amounts of data. The presented BHM approach can also be used to predict the remaining useful life (RUL) of bearings both in situations in which the bearing is considered to be in a healthy state and in situations after a defect has been detected.

Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2021
Keywords
Bayesian hierarchical model, bearing life prediction, bearing life rating L10h, probability distribution, prognostics, remaining useful life
National Category
Probability Theory and Statistics
Research subject
Operation and Maintenance; Centre - SKF-LTU University Technology Cooperation; Applied Mathematics
Identifiers
urn:nbn:se:ltu:diva-68340 (URN)10.1109/ACCESS.2021.3130157 (DOI)000724471400001 ()2-s2.0-85120049117 (Scopus ID)
Projects
SKF- UTC
Note

Validerad;2021;Nivå 2;2021-12-03 (johcin)

Available from: 2018-04-13 Created: 2018-04-13 Last updated: 2023-09-05Bibliographically approved
Talebiahooie, E., Thiery, F., Meng, J., Mattsson, H., Nordlund, E. & Rantatalo, M. (2021). Modelling of Railway Sleeper Settlement under Cyclic Loading Using a Hysteretic Ballast Contact Model. Sustainability, 13(21), Article ID 12247.
Open this publication in new window or tab >>Modelling of Railway Sleeper Settlement under Cyclic Loading Using a Hysteretic Ballast Contact Model
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2021 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 13, no 21, article id 12247Article in journal (Refereed) Published
Abstract [en]

Ballasted tracks are common in the railway system as a means of providing the necessary support for the sleepers and the rails. To keep them operational, tamping and other maintenance actions are performed based on track geometry measurements. Ballast particle rearrangement, which is caused by train load, is one of the most important factors leading to track degradation. As a result, when planning maintenance, it is vital to predict the behaviour of the ballast under cyclic loading. Since ballast is a granular matter with a nonlinear and discontinuous mechanical behaviour, the discrete element method (DEM) was used in this paper to model the ballast particle rearrangement under cyclic loading. We studied the performance of linear and nonlinear models in simulating the settlement of the sleeper, the lateral deformation of the ballast shoulder and the porosity changes under the sleeper. The models were evaluated based on their ability to mimic the ballast degradation pattern in vertical and lateral direction. The linear contact model and the hysteretic contact model were used in the simulations, and the effect of the friction coefficient and different damping models on the simulations was assessed. An outcome of this study was that a nonlinear model was proposed in which both the linear and the hysteretic contact models are combined. The simulation of the sleeper settlement and the changes in the porosity under the sleeper improved in the proposed nonlinear model, while the computation time required for the proposed model decreased compared to that required for the linear model.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
ballasted track, linear contact model, hysteretic contact model, cyclic loading, DEM
National Category
Geotechnical Engineering Infrastructure Engineering
Research subject
Operation and Maintenance; Soil Mechanics; Mining and Rock Engineering
Identifiers
urn:nbn:se:ltu:diva-87896 (URN)10.3390/su132112247 (DOI)000726596200001 ()2-s2.0-85118542427 (Scopus ID)
Funder
Luleå Railway Research Centre (JVTC)
Note

Validerad;2021;Nivå 2;2021-11-15 (beamah)

Available from: 2021-11-15 Created: 2021-11-15 Last updated: 2023-09-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8471-4494

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