An SVM-BaLSTM-Based Remaining Useful Life Prediction Method for SPD in IIoT

Surge protective devices (SPDs) play a crucial role in Industrial Internet of Things (IIoT) by protecting equipment from lightning-induced overvoltage. However, lightning surge can cause SPD damage, thus interfering with the operation of equipments in IIoT, causing huge losses. Therefore, accurate prediction of SPD RUL is a key technology to ensure the safety of the equipments in IIoT. We conduct degradation testing on SPD to extract degradation parameters and model the degradation curve. Subsequently, we introduce a SPD RUL prediction model utilizing SVM-BaLSTM. Our approach integrates SPD degradation curve with RUL prediction models to enhance predictive accuracy. Furthermore, we establish a real-time monitoring system for SPD to gather degradation parameters, facilitate data input for RUL prediction models. Our experimental results demonstrate a high degree of accuracy, with a root mean square error (RMSE) of approximately 0.00098, indicating a close alignment between predicted and actual RUL values, and in contrast experiments with other prediction models, our prediction model also shows the best performance.