Gradient-enhanced physics-informed long short-term memory networks for stable and accurate prediction of the RUL of electronic components

For effective maintenance decisions on electronic components, operators need predictions of the Remaining Useful Life (RUL) that are not only accurate but also stable. To this aim, a novel prognostic model based on gradient-enhanced Physics-Informed Neural Networks (gPINNs) is developed. It is based on mathematically formulating the physical fact that the ground-truth RUL of a component is reduced by one time unit for every unit of time elapsed in its life and its incorporation into the loss function of a Physics-Informed Long Short-Term Memory (PILSTM) network. To recover from possible inaccurate RUL predictions, which can occur especially at the end of the component life, a gradient-enhanced PILSTM is developed by considering the second derivative of the RUL, which should ideally be null. Additionally, a novel ensemble strategy is proposed for automatically weighing the different terms of the loss function so as to eliminate the labor-intensive and error-prone process of manually tuning the weights and to further improve the accuracy of the predictions. The proposed method is applied to two types of electronic components: Insulated Gate Bipolar Transistors (IGBTs) and lithium-ion batteries. The results demonstrate that it outperforms other state-of-the-art methods in terms of accuracy and stability of the RUL predictions.