Advanced Fault Diagnosis in Rotary Machines Using Optimized Transfer Learning

Rotary machine fault diagnosis is essential for maintaining system performance and ensuring operational safety. Deep data–driven models have demonstrated exceptional performance in fault diagnosis of rotary machines when sufficient training data is available. However, obtaining this data, particularly under faulty conditions, can be labor-intensive and time-consuming. The challenges posed by a data-driven model with insufficient training data have been addressed by transfer learning (TL) techniques when similar machine data is available. However, these conventional TL-based models struggle to perform effectively for a machine where gathering similar training data is difficult. This paper presents a deep TL approach with an optimized fine-tuning strategy, enabling the effective transfer of knowledge from a source domain with diverse machine data to a target domain with limited data from target machines. The proposed framework introduces a feature-space guided fine-tuning strategy in which the optimal retraining depth is determined through quantitative evaluation of target-domain feature separability across successive pretrained layers using silhouette score analysis. This transforms conventional heuristic layer-freezing into an objective data-driven adaptation process, enabling controlled transfer under domain shift and limited target data availability. Experimental results using data obtained from a real industrial testbeds involving milling cutting tool, grinding machine and public Paderborn dataset validate the effectiveness of the proposed approach and demonstrate its superiority over existing TL techniques in the similar domain.