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- Kulkarni, Rohan, 1991-
(författare)
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Onboard condition monitoring of vehicle-track dynamic interaction using machine learning : Enabling the railway industry’s digital transformation
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- Kulkarni, Rohan, 1991-, et al.
(författare)
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Unsupervised rail vehicle running instability detection algorithm for passenger trains (iVRIDA)
- 2023
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Ingår i: Measurement. - : Elsevier. - 0263-2241 .- 1873-412X. ; 216, s. 112894-112894
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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