PhysFaultNet: A Physics-Informed Multimodal Network for Bearing Fault Diagnosis

As a core component of rotating machinery, bearing failures can easily lead to equipment downtime or safety accidents, making diagnosis a hot topic in industrial maintenance. PhysFaultNet innovatively combines physical prior knowledge with deep learning. Through its multimodal architecture of envelope analysis, time-series modeling, and feature space learning, it addresses the problems of traditional methods relying on manual work and pure data-driven methods having poor generalization, achieving high-precision fault detection and diagnosis and providing a new path for predictive maintenance.

Bearings bear complex loads, and signals are weak in the early stages of failure; traditional methods rely on manual analysis of time-domain/spectral features, with limitations such as strong professional dependence, inability to monitor in real time, and large subjective influence; pure data-driven methods require a large amount of labeled data, have poor interpretability, and limited generalization ability for unknown working conditions, so there is a need to balance data-driven and knowledge-driven approaches.

The core idea is to integrate the physical laws of bearing vibration (such as the relationship between fault characteristic frequency and geometry/rotational speed, modulation features, etc.); the technical architecture includes three modules: envelope analysis (Hilbert transform to extract modulation features), time-series modeling (capturing dynamic dependencies of signals), and feature space learning (unified discriminative space plus physical constraints); it innovatively adopts an anomaly detection perspective, using only normal samples for training to identify anomalies, solving the problem of scarce fault data and supporting incremental learning.

Validated on standard datasets such as Case Western Reserve University and University of Paderborn, it shows excellent performance in fault detection accuracy, type recognition precision, and early fault detection capability; in early fault scenarios, it can identify weak signal signs to achieve earlier warning; in cross-working condition generalization experiments, it maintains high accuracy under unseen rotational speed/load conditions, verifying the effectiveness of physics-informed guidance.

It can be applied to scenarios such as wind power (main bearing monitoring), rail transit (wheel set bearing safety), and intelligent manufacturing (equipment health management), promoting the transformation from passive maintenance to predictive maintenance, reducing costs and improving efficiency; the idea of physical information fusion can be extended to the diagnosis of components such as gears and motor rotors, which is an important direction for industrial AI.

PhysFaultNet integrates physical priors with deep learning to improve interpretability and generalization ability; with the development of the Industrial Internet of Things, such technologies will play a key role in the safe operation of equipment and the transformation of intelligent manufacturing, providing reliable technical support.