Fault detection and classification of electric vehicle motor drive using ensemble subspace k-nearest neighbour model

Fault diagnosis in electric vehicle motor drive systems is essential to ensure reliable and safe operation. Especially in voltage source inverter-fed permanent magnet synchronous motor drives, switch faults need attention as they are the most fault-prone. Furthermore, the power switch faults, including open-circuit and short-circuit conditions, can significantly affect system performance. However, existing model- and signal-based fault diagnosis techniques often face limitations such as dependency on precise modelling and often give limited accuracy under dynamic operating conditions. On the contrary, existing data-driven approaches experience difficulties from feature redundancy and reduced generalisation in high-dimensional datasets. To address these challenges, this paper proposes an ensemble subspace k-nearest neighbour model for fault detection and classification of switch open-circuit and short-circuit faults. From a developed simulation model, the datasets under normal and multiple fault conditions (F0–F8), incorporating variables such as three-phase currents, are collected. Furthermore, the statistical features are extracted and subsequently reduced using principal component analysis to enhance feature representation and computational efficiency. The proposed model is evaluated using fivefold cross-validation and compared with other machine learning approaches, including bagged trees, quadratic support vector machine, boosted trees, and a neural network. The results reveal that the proposed subspace k-nearest neighbour model attains a testing classification accuracy of 98.49%, with an average precision of 97.5%, a recall of 98.3%, and an F1 score of 97.8%, surpassing the comparative models. These results denote that the proposed subspace k-nearest neighbour provides an effective and computationally efficient solution for reliable fault diagnosis in electric vehicle motor drive systems.