Motor bearing fault diagnosis based on industrial internet of things and transfer learning

Introduction Motor bearing faults seriously affect industrial safety and efficiency. Traditional diagnosis methods often lack adaptability across conditions and rely heavily on labeled data. This study proposes a fault diagnosis model integrating the Industrial Internet of Things (IIoT) and transfer learning to improve robustness and generalization. Methods The model combines CNN-BiGRU for spatio-temporal feature extraction, an adaptive multi-source feature fusion mechanism (Adaboost with dynamic cutting), and a dual-source domain transfer module based on joint maximum mean discrepancy (JMMD). Embedded in an IIoT platform, it supports real-time sensing, cross-domain adaptation, and closed-loop diagnosis-optimization. Results Using the Jiangnan University bearing dataset, the model achieved 94.7% diagnostic accuracy with a 6.8% false alarm rate, and a maximum state recognition match of 97.3%. In practical tests, accuracy remained above 93% under diverse conditions, with a response time of 19.6 s and GPU usage of only 8.9%. Compared with CNN, SqNet, and K-GCN, the proposed method showed superior robustness, efficiency, and cross-condition generalization. Discussion The IIoT-TL model effectively enhances predictive maintenance by reducing costs and improving equipment stability. While it depends on sensor configurations and has not been tested on more complex heterogeneous devices, it demonstrates strong potential for real-time deployment in industrial systems. Future work will explore lightweight frameworks and broader application scenarios.