A Novel Portable Edge-Intelligent System for Cross-Individual Fault Diagnosis

Intelligent fault diagnosis has received widespread attention and is of great significance for the safety and stability of industrial systems. However, most current studies are limited to algorithm design and lack systematic deployment schemes, especially for the design of intelligent diagnostic instruments. Besides, the current verification benchmark based on source device fault diagnosis (SDFD) ignores potential individual differences, resulting in degraded algorithm generalization for unseen individual devices and combinations of compound faults. This article proposes a novel design scheme of a portable edge-intelligent system, including a portable intelligent diagnostic instrument (PIDI) and a novel refined trigonometric activation representation transformer (RTART). The designed PIDI not only inherits the functions of data acquisition and storage, but also conveniently supports real-time inference and updates of deep learning algorithms, as well as human–machine interaction capabilities. A novel RTART model is proposed to adapt to the characteristics of vibration signals through TrigAR, and enhance cross-individual feature generalization ability via a multiscale region pruning (MSRP) strategy. Finally, the measurement and validation benchmark for intelligent fault diagnosis is rethought to establish cross-individual fault diagnosis (CIFD). The superiority of the proposed RTART is first validated on public datasets based on CIFD, achieving cross-individual accuracy (CIA) rates of 96.04%, which is superior to the compared advanced models. The designed PIDI embedded with RTART is deployed on real industrial production lines, achieving a CIA of 92.55%, further verifying the effectiveness and application potential of the proposed edge-intelligent system.