Learnable Parallel Wavelets With Orthogonality Constraints: A Noise-Robust Deep Learning Architecture for Neutron Chopper Fault Diagnosis

Spallation neutron sources are among the rarest and most advanced research infrastructures in the world, with fewer than five large-scale facilities in operation globally. Neutron choppers, as mission-critical components within such systems, must operate continuously under extreme conditions—including strong radiation, low vacuum, and high rotational inertia. These constraints make conventional fault diagnosis approaches ineffective, as sensors cannot be installed near the fault-prone areas (e.g., bearing housings), but instead must be placed remotely due to radiation shielding. This leads to long signal transmission paths, structural discontinuities, and severely degraded signal-to-noise ratios (SNRs), posing substantial challenges for fault diagnosis and predictive maintenance. To address this unique and high-stakes problem, we propose LPWOC (Learnable Parallel Wavelets with Orthogonality Constraints), a noise-robust deep learning model that learns adaptive wavelet filter banks and thresholding functions directly from vibration data. By incorporating conjugate quadrature filters with orthogonality regularization and fully learnable denoising layers, LPWOC offers enhanced feature diversity, low computational complexity, and exceptional resilience to noise. Experiments on a dedicated neutron chopper testbed—featuring realistic sensor placement and seven bearing health statuses—demonstrate 99.21% accuracy under low-SNR conditions, outperforming five state-of-the-art methods. This work provides a scalable and deployable diagnostic solution for one of the most demanding industrial environments in existence.