Convolutional & Spiking Neural Networks for the Efficient Classification of Rotor Faults in Induction Motors via Stator Current and Stray Flux Signals

Abstract: Fault detection and diagnostics for electrical machines have attracted significant research interests over the last two decades, as electric motors and generators dominate the industrial setting and electromobility applications within Industry 4.0. At the same time, recent advances in artificial intelligence (AI) and smart technologies for diagnostics and prognostics are utilised in tandem with machine learning for purposes of condition monitoring in electromechanical devices and electric drives. To this end, this work presents the application of neural networks for the health assessment and classification of induction motors that are suffering from rotor electrical faults, notably rotor cage asymmetries such as broken rotor bars. The proposed methods are performed in various loading conditions with data acquired by extensive transient electromagnetic simulations via finite element method (FEM) software. Two different neural network architectures are used to achieve a reliable diagnostic outcome regardless the load condition of the machine. This includes the no-load case, and relies solely on non-intrusive monitoring with minimally processed stator current and stray flux signals.

Recommended citation: H. Xia et al., ”Convolutional & Spiking Neural Networks for the Efficient Classification of Rotor Faults in Induction Motors via Stator Current and Stray Flux Signals,” in 2025 IEEE Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), Aug. 2025, pp. 1-7.
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