Ensuring the reliable operation of programmable logic controller control units is essential for industrial system safety. Traditional diagnostic approaches, which rely on manual inspection and delayed response, prove inadequate for the predictive maintenance requirements of smart manufacturing. This study tackles challenges in multi-sensor data fusion and model adaptability by developing an intelligent framework that combines digital twin technology with meta-learning. The approach integrates vibration and current signals through an attention-based fusion network, enabling rapid adaptation to new equipment with minimal data samples. Experimental results demonstrate exceptional performance: a Matthews correlation coefficient of 0.965 for fault classification and a root mean square error of 0.048 for remaining useful life prediction. These results significantly surpass current state-of-the-art methods, improving diagnostic accuracy by over 3.4% and prediction precision by more than 17% compared to the best existing baseline.
With the rapid advancement of Industry 4.0, anomaly detection secures critical infrastructure by monitoring and analysing sensor time-series data. However, existing anomaly detection methods are often suffering from incomplete temporal feature extraction and delayed detection responses. To overcome these issues, we introduce conditional variational AutoEncoder-based anomaly detection (CVAD). CVAD enhances temporal pattern learning by utilising an improved CVAE that jointly processes raw time-series data and their corresponding frequency-domain representations. To maintain robustness under concept drift, we implement a self-adjusting threshold mechanism that dynamically adjusts detection criteria, eliminating the overhead of model retraining. Experimental results on the SensorScope dataset, NASA-SMAP dataset, and NASA-MSL dataset demonstrate that the proposed model outperforms seven baseline methods in terms of the F1 score: compared with the second-best baseline TimesNet (89.45% on SensorScope, 82.99% on NASA-SMAP, 89.79% on NASA-MSL), CVAD achieves 93.35%, 93.45%, and 89.84%, respectively - representing improvements of 3.9%, 10.46%, and 0.05% in F1 score.
