Convolutional-LSTM for IGBTs Prognostics and Age Monitoring : Designing a neural network for predicting aging precursors in power devices

Abstract: In recent years, extensive research efforts have been dedicated to the field of prognostics and age-related degradation, with major focus on higher complexity devices. However, relatively little attention has been given to power devices, such as Insulated Bipolar Gate Transistors (IGBTs), despite their critical role in high power electronic applications. These device find their application in various domains, including power grids, where their capability of operating over a broad spectrum of current and voltage levels is a necessity. Because of their central role, their condition can heavily effect the entire system, and the lack of comprehensive understanding and accurate aging prediction for IGBTs poses a significant challenge in ensuring their optimal performance, the deployment of intelligent equipment maintenance and in minimizing the risk of failure. To overcome this research and knowledge gap, the present study focuses on the development and implementation of a Convolutional-Long Short-Term Memory Neural Network, for predicting the value of the component temperature, as the main precursor for its premature aging. Moreover, an incremental learning approach is employed to address the challenges of online learning in real-world scenarios. To evaluate the proposed methodology, a comparative analysis is conducted against a base Long Short-Term Memory (LSTM) model, using an IGBT data set from the NASA Ames Laboratory. The empirical experiments yield promising results, demonstrating that the proposed model outperforms the base LSTM model in terms of accuracy and predictive capabilities. Moreover, the incremental approach appears to be suitable to extend the Convolutional-LSTM model to online learning settings. The findings of this research provide valuable insight into prognostics of power devices and contribute to broaden the field of predictive maintenance, especially in the context of power devices.