Physics-Informed Multi-Scale Network with Loss-Guided Curriculum Learning for Robust Fault Diagnosis

Panfeng Bao, Wenjun Yi, Yue Zhu, Yufeng Shen, Haotian Peng — Sun, 05 Jul 2026 00:00:00 +0000

Reliable fault diagnosis of rotating machinery is critical, yet early weak fault impulses are frequently buried in severe compound interference from mechanical harmonics and environmental noise. To address this, a novel Physics-Informed Multi-Scale Network (PI-MSN) is proposed. Variational Mode Decomposition (VMD) is first employed to decouple raw signals into distinct physical frequency bands. Subsequently, a Physics-Informed Channel Attention (PICA) module jointly evaluates the Kurtosis and Root Mean Square of each channel to autonomously highlight fault impulses and suppress harmonic interference. A Multi-Scale Feature Extractor then captures comprehensive fault characteristics. Furthermore, a closed-loop Loss-Guided Smooth Interference Scheduler (LGSIS) dynamically regulates injected interference during training based on real-time loss, fundamentally eradicating catastrophic forgetting. Extensive experiments on the CWRU and HUST datasets demonstrate the framework's exceptional robustness. The highly lightweight PI-MSN achieves state-of-the-art diagnostic accuracy, sustaining over 98\% accuracy even under severe -4 dB compound interference, proving that physical interpretability effectively eliminates the reliance on massive parameter stacking.

International Journal of Prognostics and Health Management

Physics-Informed Multi-Scale Network with Loss-Guided Curriculum Learning for Robust Fault Diagnosis