ML-Leveraged System-Wide Fault Diagnosis Method for Wireless Power Transfer

This paper proposes a system-wide fault diagnosis method for wireless power transfer (WPT) systems. This method enables the comprehensive fault diagnosis of key components in WPT systems by using only a single current sensor. It requires no controller upgrades, offering a cost-effective and minimally invasive solution. The fault diagnosis method is based on a support vector machine (SVM) algorithm; the Hierarchy-SVM algorithm is proposed which reduces training time to 54% and recognition time to 16.5% of those required by traditional multi-class SVM algorithms, while maintaining comparable accuracy, which was tested under the same dataset and hardware configuration. Lastly, experimental verification is conducted. The experimental results demonstrate that the proposed method achieves a more than 95% accuracy rate in identifying various faults, with an average single identification time of average 14.19 ms.