Automated broad transfer learning for cross-domain fault diagnosis

Smart manufacturing system pursues automated modeling algorithms for industrial applications in dynamic environments. The prevalent deep transfer learning (DTL) has achieved promising results in cross-domain fault diagnosis. However, most DTL algorithms are dataset- and domain-specific. They require hyperparameter optimization (HPO) calling for prior knowledge to accomplish a promising prediction performance. This dilemma persists when new data from different domains arrive. An automated broad-transfer learning algorithm (AutoBTL) is proposed to improve predictive modeling for cross-domain tasks. AutoBTL includes three components, a broad classifier, an active estimator, and a hyperparameter optimizer for solving the HPO problem in cross-domain fault diagnosis. At each iteration, AutoBTL initiates a gated broad architecture assigned by the optimizer for target prediction. Then, an active estimator samples the target data with reliable pseudo labels for domain adaptation and performance evaluation. Finally, the optimizer updates a surrogate model and optimizes the hyperparameter space. These steps get repeated until satisfactory model is generated. AutoBTL involves a transductive joint validation strategy, which significantly improves the performance of the existing HPO algorithms in cross-domain tasks. The performance of the AutoBTL algorithm is validated with three benchmark datasets, including 14 cross-domain tasks. The computational results have demonstrated the accuracy and efficiency of the proposed algorithm over the widely used predictive modeling algorithms.