Fault detection and isolation method for vehicle running instability from vehicle dynamics response using machine learning

• In this paper, a Fault Detection and Isolation (FDI) method is proposed for monitoring the vehicle running stability in a high-speed railway bogie. The objective is to detect and isolate the different faults of bogie components which are critical to vehicle stability, especially degraded yaw dampers and high equivalent conicity caused by wheel wear. The proposed method has two steps; firstly, signal features sensitive to the characteristics of running instability are extracted based on frequency domain and time domain analysis of lateral accelerations of bogie frame and axlebox; then these features along with vehicle speed are fed into machine learning based fault classifiers. The supervised machine learning based fault classifier are trained to identify the cause of observed running instability among yaw damper degradation and wheel-rail profile pair with high equivalent conicity. The Support Vector Machine (SVM) classifier with Linear and Gaussian kernels are trained by k-fold crossvalidation method and the hyperparameters are optimized with a bayesian optimization algorithm to minimize the classification error. These fault classifiers are trained and tested with an extensive database generated from numerical experiments performed by multibody simulation (MBS) software. The performance of Linear and Gaussian SVM fault classifiers is compared with each other to identify the best performing classifier. The results underline the ability of machine learning based fault classifiers to be used for FDI of vehicle running instability and outline the possibility of detecting and isolating bogie faults critical to the vehicle stability based on onboard measurement of vehicle dynamic response.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Farkostteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Vehicle Engineering (hsv//eng)

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