Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment. They have also made artificial intelligence incorporated as a major facilitator. Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure, and thus timely maintenance can ensure safe operations. Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model, which typically involves two datasets. In response to the availability of multiple datasets, this paper proposes using selective and adaptive incremental transfer learning (SA-ITL), which fuses three algorithms, namely, the hybrid selective algorithm, the transferability enhancement algorithm, and the incremental transfer learning algorithm. It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer. The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time. The proposed algorithm is evaluated and analyzed using ten benchmark datasets. Compared with other algorithms from existing works, SA-ITL improves the accuracy of all datasets. Ablation studies present the accuracy enhancements of the SA-ITL, including the hybrid selective algorithm (1.22%–3.82%), transferability enhancement algorithm (1.91%–4.15%), and incremental transfer learning algorithm (0.605%–2.68%). These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.