Sajjadi Pooya, Dinmohammadi Fateme, Shafiee Mahmood
School of Computing and Engineering, University of West London, London W5 5RF, UK.
School of Engineering, University of Surrey, Guildford GU2 7XH, UK.
Sensors (Basel). 2025 Jul 4;25(13):4164. doi: 10.3390/s25134164.
As industries become increasingly dependent on cyber-physical systems (CPSs), failures within these systems can cause significant operational disruptions, underscoring the critical need for effective Prognostics and Health Management (PHM). The large volume of data generated by CPSs has made deep learning (DL) methods an attractive solution; however, imbalanced datasets and the limited availability of fault-labeled data continue to hinder their effective deployment in real-world applications. To address these challenges, this paper proposes a transfer learning approach using a pre-trained transformer architecture to enhance fault detection performance in CPSs. A streamlined transformer model is first pre-trained on a large-scale source dataset and then fine-tuned end-to-end on a smaller dataset with a differing data distribution. This approach enables the transfer of diagnostic knowledge from controlled laboratory environments to real-world operational settings, effectively addressing the domain shift challenge commonly encountered in industrial CPSs. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on publicly available datasets generated from a laboratory-scale replica of a modern industrial water purification facility. The results show that the model achieves an average F1-score of 93.38% under K-fold cross-validation, outperforming baseline models such as CNN and LSTM architectures, and demonstrating the practicality of applying transformer-based transfer learning in industrial settings with limited fault data. To enhance transparency and better understand the model's decision process, SHAP is applied for explainable AI (XAI).
随着各行业对网络物理系统（CPS）的依赖日益增加，这些系统中的故障可能会导致严重的运营中断，这凸显了对有效的预测与健康管理（PHM）的迫切需求。CPS产生的大量数据使深度学习（DL）方法成为一种有吸引力的解决方案；然而，数据集不平衡以及故障标记数据的可用性有限，仍然阻碍着它们在实际应用中的有效部署。为应对这些挑战，本文提出一种使用预训练变压器架构的迁移学习方法，以提高CPS中的故障检测性能。首先在大规模源数据集上对一个简化的变压器模型进行预训练，然后在数据分布不同的较小数据集上进行端到端微调。这种方法能够将诊断知识从受控的实验室环境转移到实际运行环境，有效应对工业CPS中常见的领域转移挑战。为评估所提方法的有效性，在由现代工业水净化设施的实验室规模复制品生成的公开可用数据集上进行了大量实验。结果表明，该模型在K折交叉验证下的平均F1分数达到93.38%，优于CNN和LSTM架构等基线模型，并证明了在故障数据有限的工业环境中应用基于变压器的迁移学习的实用性。为提高透明度并更好地理解模型的决策过程，将SHAP应用于可解释人工智能（XAI）。
