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.

In intelligent fault diagnosis for construction machinery, robust and precise detection of out-of-distribution (OOD) data is crucial for enhancing operational efficiency and reducing downtime. This article introduces the energy-driven graph neural OOD (EGN-OOD) detector, a novel framework designed to address the complexities of OOD data in dynamic Internet of Things (IoT) environments. By integrating graph neural networks with energy-based models, our approach captures intricate fault correlations and improves the accuracy of fault diagnosis. The EGN-OOD framework uses the maximal information coefficient to transform sensor-acquired vibration data, typical in IoT applications, into graph representations. This conversion produces an adjacency matrix that outlines the nonlinear interactions among different fault types. Additionally, the framework includes an energy score-based OOD detection module that redefines classifier logits to create an energy function, enabling precise differentiation between in-distribution (ID) and OOD data. To enhance model robustness in semi-supervised settings, a propagation mechanism-based energy score update scheme is implemented, iteratively refining energy values within the graph. Empirical validation on a framework for monitoring mechanical equipment bearing wear demonstrates the EGN-OOD framework’s exceptional ability to detect and diagnose various fault conditions. This validation confirms the framework’s robust generalization capabilities and precision in fault detection and underscores its integration within IoT infrastructures, facilitating smarter diagnostic processes. The results provide substantial technical support for the intelligent diagnosis of construction machinery, advancing IoT-driven solutions for sustainable and intelligent construction practices.

Traditional machine fault diagnosis techniques are labor-intensive and hard for nonexperts to use. In this paper, a novel three-stage intelligent fault diagnosis approach is proposed for practical industrial process monitoring. A new feature processing technique is developed to enhance the identification accuracy and reduce the computation burden, which incorporates variational mode decomposition-based trend detection and self-weight algorithm. Furthermore, an adaptive density peaks search (ADPS) algorithm has been primarily proposed for adaptive clustering, whose effectiveness is verified in comparison with the original DPS, affinity propagation clustering, and K-medoids. The three-stage intelligent fault diagnosis approach is subsequently applied to three specific industrial cases. Results of bearing and gear fault diagnosis have well demonstrated that the proposed method is able to reliably and accurately identify different faults with less prior knowledge and diagnostic expertise. Moreover, the proposed technique can be adopted to adaptively monitor different conditions using unlabeled bearing run-to-failure testing data, which also shows it is well suitable for industrial online applications.