Bridging the Subpopulation Gap: A New Paradigm for Robust Fault Diagnosis in Rotating Machinery

This article introduces a ground breaking approach to rotating machinery fault diagnosis by addressing the critical, yet unexplored challenge of subpopulation shift. We present the first study to consider this in the domain, introducing a novel framework combining label propagation with time–frequency consistency regularization. Motivated by limitations of existing domain adaptation methods, we propose a unique dataset partitioning strategy that models subpopulation structures within fault categories. Our approach leverages a bridging distribution to facilitate knowledge transfer across domains with different subpopulation compositions. Theoretical analysis provides performance guarantees, while experiments on real-world bearing datasets demonstrate superior performance across various transfer learning scenarios. The proposed method consistently outperforms state-of-the-art techniques in multiple adaptation settings. By pioneering subpopulation shift consideration and introducing an innovative dataset preparation method, this work significantly advances rotating machinery fault diagnosis, offering a more reliable solution for complex industrial applications. The proposed framework directly addresses critical industrial challenges by enabling robust fault diagnosis across varying operating conditions, which helps reduce maintenance costs and prevent unexpected equipment failures in manufacturing plants.