Condition monitoring of track geometry irregularities from onboard measurements is a cost-effective method for daily surveillance of track quality. The monitoring of Alignment Level (AL) and Cross Level (CL) track irregularities is challenging due to the nonlinearities of the contact between wheels and rails. Recently, the authors proposed a signal-based method in combination with a machine learning (ML) fault classifier 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 classifier 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 classification accuracy of 95.9 ± 0.2%and kappa value of 80.4 ± 0.6%. Moreover, the Linear SVM (L SVM) and Gaussian-SVM (G SVM) classifiers give similar performance 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 implementing statistical fault classifiers to monitor the track irregularities based on dynamics in the lateral plane via in-service vehicles. The proposed method contributes towards digitalization of rail networks through condition-based and predictive maintenance.
Monitoring of Alignment Level (AL)and Cross Level (CL) Track Geometry Irregularities from Onboard Vehicle Dynamics Measurements Using Probabilistic Fault Classifier
De Rosa A.;Di Gialleonardo E.;Facchinetti A.;Bruni S.
2022-01-01
Abstract
Condition monitoring of track geometry irregularities from onboard measurements is a cost-effective method for daily surveillance of track quality. The monitoring of Alignment Level (AL) and Cross Level (CL) track irregularities is challenging due to the nonlinearities of the contact between wheels and rails. Recently, the authors proposed a signal-based method in combination with a machine learning (ML) fault classifier 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 classifier 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 classification accuracy of 95.9 ± 0.2%and kappa value of 80.4 ± 0.6%. Moreover, the Linear SVM (L SVM) and Gaussian-SVM (G SVM) classifiers give similar performance 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 implementing statistical fault classifiers to monitor the track irregularities based on dynamics in the lateral plane via in-service vehicles. The proposed method contributes towards digitalization of rail networks through condition-based and predictive maintenance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
