Knowledge Embedded Lightweight Vision Transformer for Machine Condition Monitoring

Abstract

Machine condition monitoring methods are a powerful tool for the physical condition evaluation of high-end equipment. As one of the main manufacturing applications of machine condition monitoring, tool performance evaluation (TPE) suffers the limitation of model generalization ability across multiple operating conditions. Subsequently, designing a method that can meet the diversified industrial scenarios’ requirements to confirm the cutters’ machinability is of great practical significance. However, recent TPE studies indicate that the on-machine measurement processes are predominantly affected by the interference of ambient noise and signal aliasing of multi-source sensors. Recent advances in knowledge-embedding and artificial intelligence bring new vitality to the development of industrial condition-based maintenance applications. Considering the above-mentioned analysis, this paper proposes a new knowledge-embedded intelligent TPE method, where the prior knowledge of machining parameters is fused with features extracted from multiple sensor information. The proposed method gives an effective solution for TPE tasks under multiple working conditions.