Multitask Correlation Constrained Topological Learning Toward Smart Prognostic and Health Management in IoT

• Published in: IEEE internet of things journal 2024-12, Vol.11 (24), p.39487-39496
• Main Authors: Zheng, Xuzhe, Zhou, Xiaokang, Liang, Wei, I-Kai Wang, Kevin
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Condition monitoring; Constraints; Data models; Data structures; Deep learning; energy Internet of Things (IoT); Extreme values; Feature extraction; imbalanced learning; Internet of Things; Learning; Maintenance; Modules; multitask learning (MTL); Multitasking; Network topologies; Performance evaluation; prognostic and health management (PHM); Prognostics and health management; Real time; Structural health monitoring; Structured data; Task analysis; Task complexity
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3446551

Due to the high dependence of social development on electricity, the failure of energy system equipment often leads to inestimable losses. The use of Internet of Things (IoT) technology to collect real-time data from energy devices and artificial intelligence (AI) technology to prognostic faults in the devices becomes essential to achieve power system security. Although real-time data contains rich descriptions of relevant equipment faults and solutions, the complex data structure and severe imbalance of condition monitoring (CM) data may lead to poor performance of AI models. In this study, we propose a multitask correlation constrained topology learning model for smart prognostic and health management in IoT. In particular, the proposed model mainly includes a feature extraction module, a multitask topology network (MTTN) module, and a class balance loss (CBL) algorithm module. First, the Bi-LSTM is employed to extract word collocation features and numerical features of the data, focusing on the important semantic features in complex structured data. Second, the MTTN is constructed to efficiently utilize the topological dependency between multiple tasks. Finally, a CBL loss function is applied to enhance the focus on the minority classes. Experiment and evaluation results demonstrate that our model has superior learning efficiency and prediction performance, especially on extremely imbalanced data, which can correctly predict the faulty equipment, the fault cause and the fault severity level, providing a rapid and precise reference for advance repair or replacement of energy system equipment in IoT environments.