Abstract
Faults that develop in vehicle sensors have the potential to propagate unchecked throughout control systems if undetected. Automatic fault diagnosis and health monitoring algorithms will become necessary as automotive applications become more autonomous. The current fault diagnosis systems are not effective for complex systems such as autonomous cars where the case of simultaneous faults in different sensors is highly possible. Therefore, this paper proposes a novel fault detection, isolation and identification architecture for multi-fault in multi-sensor systems with an efficient computational burden for real-time implementation. Support Vector Machine techniques are used to detect and identify faults in sensors for autonomous vehicle control systems. In addition, to identify degrading performance in a sensor and predict the time at which a fault will occur, a novel predictive algorithm is proposed. The effectiveness and accuracy of the architecture in detecting and identifying multiple faults as well as the accuracy of the proposed predictive fault detection algorithm are verified through a MATLAB/IPG CarMaker co-simulation platform. The results present detection and identification accuracies of 94.94% and 97.01%, respectively, as well as a prediction accuracy of 75.35%.
Highlights
With the advent of autonomous vehicles, the number of sensors within cars has been significantly increased
Support Vector Machines (SVM) are a statistical learning method that can identify a separating hyperplane between faulty and normal data. Using this plane to determine whether new data are faulty or not, they can provide greater generalisation capability compared to Artificial Neural Networks (ANN) and k-Nearest Neighbours (k-NN)
Key observations have been made on the application of SVM models in multi-sensor control systems, most notably showing that different sensors have different detection and identification accuracies when exposed to particular fault types such as drift faults
Summary
With the advent of autonomous vehicles, the number of sensors within cars has been significantly increased. SVMs are a statistical learning method that can identify a separating hyperplane between faulty and normal data Using this plane to determine whether new data are faulty or not, they can provide greater generalisation capability compared to ANNs and k-NNs. Current data-driven FDII architectures can be categorised as single fault detection for a single sensor [29], single fault detection of multi-sensor systems [30,31,32] and multi fault detection and identification of a single sensor [20,21,22]. Despite significant recent interest in data-driven approaches for fault detection and identification applications, there are still gaps remaining in the current research with regards to multi-fault identification, detection of faults in multi-sensor systems as well as condition-based predictive fault detection.
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