Gradient-enhanced physics-informed long short-term memory networks for stable and accurate prediction of the RUL of electronic components
提出一种梯度增强物理信息长短期记忆网络，通过引入物理规律和自动权重调整，提高电子元件剩余使用寿命预测的准确性和稳定性，在IGBT和锂电池上验证有效。
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.