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Onboard condition monitoring of vehicle-track dynamic interaction using machine learning: Enabling the railway industry’s digital transformation
KTH, School of Engineering Sciences (SCI), Engineering Mechanics. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.ORCID iD: 0000-0001-5644-248x
2023 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Tillståndsövervakning ombord av dynamisk interaktion mellan fordon och spår med hjälp av maskininlärning : Möjliggörande av järnvägsbranschens digitala transformation (Swedish)
Abstract [en]

The railway sector’s reliability, availability, maintainability, and safety (RAMS) can significantly improve by adopting condition based maintenance (CBM). In the CBM regime, maintenance decisions are driven by condition monitoring (CM) of the asset. This thesis proposes machine learning (ML) based onboard CM (OCM) algorithms for CM of vehicle-track dynamic interaction via vehicle response (VR). More specifically, the algorithms are developed to monitor track irregularities (TI) and vehicle running instability incidences (VRII) via VR.

CM of TI from onboard accelerations is a cost-effective method for daily surveillance of tracks. Most of the latest research is focused on monitoring vertical irregularity via vertical accelerations. Less attention is given to monitoring alignment level (AL) and cross level (CL) track irregularities. The PhD thesis proposes an ML based OCM algorithm to identify track sections with AL and CL  track irregularities exceeding maintenance thresholds via bogie frame accelerations (BFAs). In this thesis, the OCM algorithm’s supervised ML models are trained on BFAs’ datasets synthesized with multibody simulation (MBS) of a high-speed diagnostic vehicle. Furthermore, the trained ML models and OCM algorithm are validated with measurements acquired by the same high-speed vehicle. The proposed OCM algorithm shows excellent performance in track quality surveillance only from BFAs. 

OCM of vehicle running instability (VRI) is important to ensure safety and onboard ride comfort. The latest research focuses on designing OCM algorithms for detecting VRI, but these OCM algorithms lack fault diagnosis (FD) of detected VRII. The PhD thesis proposes various OCM algorithms under an "intelligent vehicle running instability detection algorithm" (iVRIDA) umbrella to detect VRII and diagnose corresponding root causes via carbody accelerations. The occurrence of VRI during regular operation across a whole train fleet is an anomaly. Thus, an unsupervised anomaly detection (AD) based iVRIDA algorithm is proposed and later extended as iVRIDA-fleet for vehicle fleetwide application. The proposed OCM algorithms iVRIDA and iVRIDA-fleet are verified by onboard measurements of a European high-speed vehicle and the Swedish X2000 vehicle fleet.

The thesis contributes towards the digitalization of vehicle and track maintenance by enabling adaptation of the CBM regime.

Abstract [sv]

Järnvägssektorns tillförlitlighet, tillgänglighet, underhållsmässighet och säkerhet (RAMS) kan förbättras avsevärt genom att införa tillståndsbaserat underhåll (CBM). I CBM-regimen drivs underhållsbeslut av tillståndsövervakning (CM) av tillgången. Denna avhandling föreslår maskininlärning (ML) baserade omboard CM (OCM) algoritmer för CM av fordon-spår dynamisk interaktion via fordonsrespons (VR). Mer specifikt utvecklas algoritmerna för att övervaka spårlägesfel (TI) och fordonsinstabilitets (VRII) via VR.

CM av TI från accelerationer ombord är en kostnadseffektiv metod för daglig övervakning av spår. Det mesta av den senaste forskningen är inriktad på att övervaka vertikal oregelbundenhet via vertikala accelerationer. Mindre uppmärksamhet ägnas åt övervakning av oegentligheter i lateralled (AL) och rälsförhöiningsfel (CL). Doktorsavhandlingen föreslår en ML-baserad OCM-algoritm för att identifiera spårsektioner med AL- och CL-spåroregelbundenheter som överskrider underhållströsklar via boggiramaccelerationer (BFA). I denna avhandling tränas OCM-algoritmens övervakade ML-modeller på BFAs' data syntetiserade med flerkropps-simulering (MBS) av ett höghastighetsfordon. Dessutom valideras de tränade ML-modellerna och OCM-algoritmen med mätningar som erhållets från samma höghastighetsfordon. Den föreslagna OCM-algoritmen visar utmärkt prestanda vid övervakning av spårkvalitet endast från BFA.

OCM för fordonets gånginstabilitet (VRI) är viktigt för att säkerställa säkerhet och komfort. Den senaste forskningen fokuserar på att designa OCM-algoritmer för att upptäcka VRI, men dessa OCM-algoritmer saknar feldiagnos (FD) av detekterad VRII. Doktorsavhandlingen föreslår olika OCM-algoritmer under ett "intelligent vehicle running instability detection algorithm" (iVRIDA) paraply för att upptäcka VRII och diagnostisera motsvarande grundorsaker via korgsaccelerationer. Förekomsten av VRI under regelbunden drift över en hel tågflotta är en anomali. Således föreslås en oövervakad anomalidetektering (AD) baserad iVRIDA algoritm och utvidgas senare som iVRIDA-fleet för fordonsparksomfattande applikation.

De föreslagna OCM-algoritmerna iVRIDA och iVRIDA-fleet verifieras genom mätningar ombord av ett europeiskt höghastighetsfordon och den svenska fordonsflottan X2000.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2023. , p. 1-95
Series
TRITA-SCI-FOU ; 2023:19
Keywords [en]
Active preventive maintenance, intelligent fault diagnosis, anomaly detection, vehicle fleet, track irregularities, vehicle hunting, wheel-rail interface.
National Category
Vehicle Engineering
Research subject
Vehicle and Maritime Engineering
Identifiers
URN: urn:nbn:se:kth:diva-326616ISBN: 978-91-8040-542-3 (print)OAI: oai:DiVA.org:kth-326616DiVA, id: diva2:1755646
Public defence
2023-06-01, https://kth-se.zoom.us/j/68259826119, F3, Lindstedtsvägen 26, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, Shift2Rail Projects IN2TRACK2, IN2TRACK3, PIVOT2Swedish Transport Administration, Research Excellence Area 1 and RV-29 via the KTH Railway GroupAvailable from: 2023-05-10 Created: 2023-05-09 Last updated: 2023-05-23Bibliographically approved
List of papers
1. Monitoring of lateral and cross level track geometry irregularities through onboard vehicle dynamics measurements using machine learning classification algorithms
Open this publication in new window or tab >>Monitoring of lateral and cross level track geometry irregularities through onboard vehicle dynamics measurements using machine learning classification algorithms
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2020 (English)In: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017Article in journal (Refereed) Published
Abstract [en]

In recent years, significant studies have focused on monitoring the track geometry irregularities through measurements of vehicle dynamics acquired onboard. Most of these studies analyse the vertical irregularity and the vertical vehicle dynamics since the lateral direction is much more challenging due to the non-linearities caused by the contact between the wheels and the rails. In the present work, a machine learning-based fault classifier for the condition monitoring of track irregularities in the lateral direction is proposed. The classifiers are trained with a dataset composed of numerical simulation results and validated with a dataset of measurements acquired by a diagnostic vehicle on the straight track sections of a high-speed line (300 km/h). Classifiers based on decision tree, linear and Gaussian support vector machine algorithms are developed and compared in terms of performance: good results are achieved with the three algorithms, especially with the Gaussian support vector machine. Even though classifiers are data driven, they retain the essence of lateral dynamics.

Keywords
High-speed railway, lateral dynamics, onboard diagnostics, fault classifier, decision tree, support vector machine
National Category
Vehicle Engineering
Research subject
Järnvägsgruppen - Fordonsteknik
Identifiers
urn:nbn:se:kth:diva-269055 (URN)10.1177/0954409720906649 (DOI)000514773500001 ()2-s2.0-85081742873 (Scopus ID)
Note

QC 20200302

Available from: 2020-02-29 Created: 2020-02-29 Last updated: 2023-05-09Bibliographically approved
2. Monitoring of Alignment Level (AL)and Cross Level (CL) track geometry irregularities from onboard vehicle dynamics measurements using probabilistic fault classifier
Open this publication in new window or tab >>Monitoring of Alignment Level (AL)and Cross Level (CL) track geometry irregularities from onboard vehicle dynamics measurements using probabilistic fault classifier
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2021 (English)In: Lecture Notes in Mechanical Engineering, Budapest: Springer Science and Business Media Deutschland GmbH , 2021, p. 479-487Conference paper, Published paper (Refereed)
Abstract [en]

Condition monitoring of track geometry irregularities from onboard measurements of vehicle response is a cost-effective method for surveilling qual-ity of track irregularities on daily basis. The monitoring of Alignment Level (AL)and Cross Level (CL) track irregularities is challenging due to the nonline-arities of the contact between wheels and rails. Recently, the authors proposed a signal-based method in combination with a machine learning (ML) fault classi-fier to monitor AL and CL track irregularities based on bogie frame accelerations. The authors concluded that the Support Vector Machine (SVM) fault classifier outperformed other traditional ML classifiers. Thus, an important question arises: Is the previously reported decision boundary an optimal boundary? The objective of this research investigation is to obtain an optimal decision boundary according to theory of probabilistic classification and compare the same against the SVM decision boundary. In this investigation, the classifiers are trained with results of numerical simulations and validated with measurements acquired by a diagnostic vehicle on straight track sections of a high-speed line (300 km/h). A fault classi-fier based on Maximum A Posterior Naïve Bayes (MAP-NB) classification is developed. It is shown that the MAP-NB classifier generates an optimal decision boundary and outperforms other classifiers in the validation phase with classifi-cation accuracy of 95.9±0.2% and kappa value of 80.4±0.6%. Moreover, the Lin-ear SVM (L SVM) and Gaussian-SVM (G SVM) classifiers give similar perfor-mance with slightly lower accuracy and kappa value. The decision boundaries of previously reported SVM based fault classifiers are very close to the optimal MAP-NB decision boundary. Thus, this further strengthens the idea of imple-menting statistical fault classifiers to monitor the track irregularities based on dynamics in the lateral plane via in-service vehicles. The proposed method con-tributes towards digitalization of rail networks through condition-based and pre-dictive maintenance.

Place, publisher, year, edition, pages
Budapest: Springer Science and Business Media Deutschland GmbH, 2021
Keywords
High-speed railway, lateral dynamics, onboard diagnostics, fault classifier, decision tree, support vector machine
National Category
Vehicle Engineering
Research subject
Järnvägsgruppen - Fordonsteknik
Identifiers
urn:nbn:se:kth:diva-299989 (URN)10.1007/978-3-031-07305-2_48 (DOI)2-s2.0-85136969475 (Scopus ID)
Conference
27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021, Virtual/Online, 17-19 August 2021
Note

QC 20210824

QC 20230626

Available from: 2021-08-21 Created: 2021-08-21 Last updated: 2023-06-26Bibliographically approved
3. Investigating the effect of the equivalent conicity function's nonlinearity on the dynamic behaviour of a rail vehicle under typical service conditions
Open this publication in new window or tab >>Investigating the effect of the equivalent conicity function's nonlinearity on the dynamic behaviour of a rail vehicle under typical service conditions
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2022 (English)In: Vehicle System Dynamics, ISSN 0042-3114, E-ISSN 1744-5159, Vol. 60, no 10, p. 3484-3503Article in journal (Refereed) Published
Abstract [en]

Generally, the equivalent conicity function (ECF) is denoted by equivalent conicity at 3mm (λ3mm) and a Nonlinearity Parameter (NP). NP describes the nonlinearity of the ECF and its influence on a vehicle design is explored thoroughly, however, NP’s role in vehicle and track maintenance is not researched yet. This paper investigates the influence of track maintenance actions on vehicle dynamics with help of NP vs λ3mm scatter plots of ECF database. The ECF database is constructed by combining measured worn wheel and rail profile pairs of the Swedish high-speed vehicle and rail network, respectively. The ECF database revealed an inverse relationship between λ3mm and NP, i.e., NP is negative for larger λ3mm values. The combination of negative NP and high λ3mm causes reduction in the vehicle’s nonlinear critical speed and vehicle often exhibit the unstable running on the Swedish rail network. Thus, the occurrence of ECF with negative NP and high λ3mm is undersirable and the undesirable ECF can be converted into desirable ECF by grinding the rail, which converts ECF’s into positive NP and low λ3mm combinations. Thus, the NP parameter along with the λ3mm must be considered in track maintenance decisions.

Place, publisher, year, edition, pages
Taylor & Francis, 2022
Keywords
Vehicle–track interaction, vehicle running instability, wheel–rail contacte, quivalent conicity (EC), nonlinear parameter (NP), wheel and rail profile maintenance
National Category
Vehicle Engineering
Research subject
Järnvägsgruppen - Fordonsteknik
Identifiers
urn:nbn:se:kth:diva-299580 (URN)10.1080/00423114.2021.1962537 (DOI)000684122100001 ()2-s2.0-85112217044 (Scopus ID)
Projects
IN2TRACK2
Funder
EU, Horizon 2020, 826255 (IN2TRACK2)
Note

QC 20210813

Available from: 2021-08-12 Created: 2021-08-12 Last updated: 2023-10-16Bibliographically approved
4. iVRIDA: intelligent Vehicle Running Instability Detection Algorithm for high-speed rail vehicles using Temporal Convolution Network: – A pilot study
Open this publication in new window or tab >>iVRIDA: intelligent Vehicle Running Instability Detection Algorithm for high-speed rail vehicles using Temporal Convolution Network: – A pilot study
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2022 (English)In: Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022 / [ed] Phuc Do; Gabriel Michau; Cordelia Ezhilarasu, PHM Society , 2022, Vol. 7, p. 269-277Conference paper, Published paper (Refereed)
Abstract [en]

Intelligent fault identification of rail vehicles from onboard measurements is of utmost importance to reduce the operating and maintenance cost of high-speed vehicles. Early identification of vehicle faults responsible for an unsafe situation, such as the instable running of highspeed vehicles, is very important to ensure the safety of operating rail vehicles. However, this task is challenging because of the nonlinear dynamics associated with multiple subsystems of the rail vehicle. The task becomes more challenging with only accelerations recorded in the carbody where, nevertheless, sensor maintenance is significantly lower compared to axlebox accelerometers. This paper proposes a Temporal Convolution Network (TCN)-based intelligent fault detection algorithm to detect rail vehicle faults. In this investigation, the classifiers are trained and tested with the results of numerical simulations of a high-speed vehicle (200 km/h). The TCN based fault classification algorithm identifies the rail vehicle faults with 98.7% accuracy. The proposed method contributes towards digitalization of rail vehicle maintenance through condition-based and predictive maintenance.

Place, publisher, year, edition, pages
PHM Society, 2022
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:kth:diva-315480 (URN)10.36001/phme.2022.v7i1.3344 (DOI)
Conference
7th European Conference of the Prognostics and Health Management Society 2022
Note

QC 20220726

Available from: 2022-07-07 Created: 2022-07-07 Last updated: 2023-05-09Bibliographically approved
5. Unsupervised rail vehicle running instability detection algorithm for passenger trains (iVRIDA)
Open this publication in new window or tab >>Unsupervised rail vehicle running instability detection algorithm for passenger trains (iVRIDA)
2023 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 216, p. 112894-112894, article id 112894Article in journal (Refereed) Published
Abstract [en]

Intelligently identifying rail vehicle faults instigating running instability from carbody floor acceleration is essential to ensure operational safety and reduce maintenance costs. However, the vehicle-track interaction's nonlinearities and scarcity of running instability occurrences complicate the task. The running instability is an anomaly in the vehicle-track interaction. Thus, we propose unsupervised anomaly detection and clustering algorithms based iVRIDA framework to detect and identify running instability and corresponding root cause. We deploy and compare the performance of the PCA-AD (baseline), Sparse Autoencoder (SAE-AD), and LSTM-Encoder-Decoder (LSTMEncDec-AD) model to detect the running instability occurrences.

Furthermore, we deploy a k-means algorithm on latent space to identify clusters associated with root causes instigating instability. We deployed the iVRIDA framework on simulated and measured accelerations of European high-speed rail vehicles where SAE-AD and LSTMEncDec-AD models showed 97% accuracy. The proposed method contributes to smart maintenance by intelligently identifying anomalous vehicle-track interaction events.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Vehicle hunting; unsupervised machine learning; Sparse Autoencoder; LSTM Encoder Decoder; worn wheel; failed yaw damper
National Category
Vehicle Engineering
Research subject
Vehicle and Maritime Engineering
Identifiers
urn:nbn:se:kth:diva-326371 (URN)10.1016/j.measurement.2023.112894 (DOI)000990508000001 ()2-s2.0-85153567490 (Scopus ID)
Projects
PIVOT2
Funder
Swedish Transport Administration, Research Excellence Area IEuropean Commission, European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 881807 (PIVOT II)
Note

QC 20230607

Available from: 2023-05-02 Created: 2023-05-02 Last updated: 2023-06-07Bibliographically approved
6. iVRIDA-fleet: Unsupervised rail vehicle runninginstability detection algorithm for passenger vehicle fleet
Open this publication in new window or tab >>iVRIDA-fleet: Unsupervised rail vehicle runninginstability detection algorithm for passenger vehicle fleet
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Identifying faults contributing to unsafe conditions, such as a high-speed railvehicle running instability, is crucial to ensuring operational safety. But the occurrence of vehicle running instability during regular operation across the whole vehicle fleet is a rare anomaly. An unsupervised anomaly detection (AD) based iVRIDA-fleet framework is therefore proposed to detect vehicle running instability and identify its root cause. The performance of Principal Component Analysis (PCA-AD, baseline model), Sparse Autoencoder (SAE-AD),and LSTM Encoder Decoder (LSTMEncDec-AD) models are evaluated to detect the occurrence of vehicle running instability. A k-means algorithm is then applied to latent space representations to identify various clusters associated with different root causes of observed vehicle running instability.The effectiveness of the proposed iVRIDA-fleet framework is demonstrated using onboard accelerations measured on a Swedish X2000 vehicle fleet. The probability of vehicle running instability occurrence is observed to be only 0.35% of onboard accelerations corresponding to 827,467 km travel distance.

Furthermore, the root causes identified by the iVRIDA-fleet framework are validated by investigating the maintenance records of the vehicles and track. It is identified that heavily worn wheels were the primary root cause of observed vehicle running instability, but the track (actual gauge and rail profiles) was also a contributing factor. The proposed algorithm contributes towards the digitalisation of vehicle and track maintenance by intelligently identifying anomalous events of the vehicle-track dynamic interaction.

Keywords
Vehicle hunting, unsupervised machine learning, Autoencoder, worn wheel, worn rail, vehicle fleet
National Category
Vehicle Engineering
Research subject
Järnvägsgruppen - Fordonsteknik; Vehicle and Maritime Engineering
Identifiers
urn:nbn:se:kth:diva-326615 (URN)
Funder
Swedish Transport Administration, Research Excellence Area via KTH Railway GroupEU, Horizon 2020, European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 101012456 (IN2TRACK3)
Note

QC 20230508

Available from: 2023-05-07 Created: 2023-05-07 Last updated: 2023-05-09Bibliographically approved

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