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Monitoring of Alignment Level (AL)and Cross Level (CL) track geometry irregularities from onboard vehicle dynamics measurements using probabilistic fault classifier
KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.ORCID iD: 0000-0001-5644-248x
(Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy)ORCID iD: 0000-0002-8559-6063
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics.ORCID iD: 0000-0002-0875-3520
KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Vehicle Dynamics.ORCID iD: 0000-0002-2571-4662
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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. p. 479-487
Keywords [en]
High-speed railway, lateral dynamics, onboard diagnostics, fault classifier, decision tree, support vector machine
National Category
Vehicle Engineering
Research subject
Järnvägsgruppen - Fordonsteknik
Identifiers
URN: urn:nbn:se:kth:diva-299989DOI: 10.1007/978-3-031-07305-2_48Scopus ID: 2-s2.0-85136969475OAI: oai:DiVA.org:kth-299989DiVA, id: diva2:1586696
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
In thesis
1. Onboard condition monitoring of vehicle-track dynamic interaction using machine learning: Enabling the railway industry’s digital transformation
Open this publication in new window or tab >>Onboard condition monitoring of vehicle-track dynamic interaction using machine learning: Enabling the railway industry’s digital transformation
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Tillståndsövervakning ombord av dynamisk interaktion mellan fordon och spår med hjälp av maskininlärning : Möjliggörande av järnvägsbranschens digitala transformation
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
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:nbn:se:kth:diva-326616 (URN)978-91-8040-542-3 (ISBN)
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 Group
Available from: 2023-05-10 Created: 2023-05-09 Last updated: 2023-05-23Bibliographically approved

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Kulkarni, RohanQazizadeh, AlirezaBerg, Mats

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Kulkarni, RohanRosa, Anna DeQazizadeh, AlirezaBerg, MatsGialleonardo, Egidio DiFaccinetti, AlanBruni, Stefano
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