The human-centric framework integrating knowledge distillation architecture with fine-tuning mechanism for equipment health monitoring

Human-centricity serves as the cornerstone of the evolution of manufacturing into Industry 5.0. Accordingly, modern manufacturing prioritizes both the well-being of human workers and their collaboration with production systems. Successful systems would be user-friendly and market-appropriate, effectively identifying and analyzing user needs. This study aims to integrate user requirements into the framework for equipment health monitoring (EHM). The proposed framework addresses issues related to insufficient training samples and variable-length data by combining an encoder-decoder architecture with an attention mechanism and a conditional generative adversarial network (EDA-CGAN). Furthermore, the authors utilize a teacher-student network to reduce model complexity through knowledge distillation (KD). To prevent negative knowledge distillation, this study incorporates user requirements using Kullback-Leibler divergence (KLD) to determine whether the teacher model would be fine-tuned. Consequently, we employ the explainable AI (XAI) to provide a clear and understandable explanation for the prediction results. Thus, the proposed human-centric EHM consisting of four modules: (i) the data augmentation (ii) the fine-tuning mechanism (ii) the equipment health prediction model (iv) the explainable AI (XAI). The authors employ these methods to uncover new research insights that are vital for advancing the methodological innovation within the proposed framework. To evaluate model performance, this study conducts an empirical investigation to illustrate the capability and practicality of the proposed framework. The results indicate that our algorithm outperforms existing machine learning models, enabling the implementation of the proposed framework in the real-world manufacturing environment to maintain equipment health.