| 1. |
- Kulkarni, Rohan, 1991-, et al.
(author)
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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
- 2021
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In: Lecture Notes in Mechanical Engineering. - Budapest : Springer Science and Business Media Deutschland GmbH. ; , s. 479-487
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Conference paper (peer-reviewed)abstract
- 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.
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| 2. |
- Rosa, Anna De, et al.
(author)
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Monitoring of lateral and cross level track geometry irregularities through onboard vehicle dynamics measurements using machine learning classification algorithms
- 2020
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In: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit. - : SAGE Publications. - 0954-4097 .- 2041-3017.
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Journal article (peer-reviewed)abstract
- 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.
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