The Unreasonable Effectiveness of Neural Operators and Mambas in Detecting and Quantifying Electrical Machine Faults: A Case Study on Eccentricity
Year: 2026
Type: article
Abstract: Reliable fault detection and quantification are essential for the operational integrity of electric machines. While traditional current-based analysis relies on harmonic signatures or wavelet-based time-frequency representations, this study investigates modern learning formulations that capture spectral, multiscale, and temporal characteristics of fault-affected signals. Moving beyond conventional models, including our earlier CNN-based approaches, we develop sequence-based and operator-learning architectures within a multi-output formulation for eccentricity fault analysis. Three models are investigated: Mamba for temporal dynamics, the Fourier Neural Operator for global spectral mapping, and the Wavelet Neural Operator for localized multiscale decomposition. Evaluated on induction, salient pole synchronous, and inverter-based reluctance synchronous machines, each model maps stator current waveforms to multiple diagnostic quantities, including voltages, operating conditions, and fault severity. With time-delay embedding, all three achieve low prediction errors, with severity RMSE reaching the 10−4 scale for the induction machine, a notable reduction from the 0.04 errors of our earlier hierarchical CNN models. These results show that modern sequence-based and operator-learning formulations can broaden machine fault analysis by enabling simultaneous prediction and estimation of multiple aspects of machine condition within a single model.
Source: Machines
Institution: University of Victoria
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7161964128