摘要：Data-driven models are currently used extensively for remaining useful life (RUL) estimation of equipment with multisensor signals. But the low controllability is one of their common limitations. This study proposes a systematic method to predict RUL with multisensor data under dynamic operating conditions and failure modes. The proposed method integrates a physics-informed loss function with data-driven methods to achieve the targets of safe and controllable predicting. A delayed prediction penalty mechanism-based loss function is introduced into the deep learning model training. Finally, the proposed method is validated on the Commercial Modular Aero-Propulsion (C-MAPSS) dataset. Comparisons with other advanced forecasting methods show that the predictions are more safety while ensuring high-fitting accuracy. The controllability and flexibility of the deep learning model are improved in practice.

关键字：RUL estimation; data preprocessing; physics-informed machine learning; neural network training

ISSN号：9781665470926

卷、期、页：500-504

发表日期：2023-11-01

期刊分区（SCI为中科院分区）：无

收录情况：EI（工程索引）

发表期刊名称：2022 6TH INTERNATIONAL CONFERENCE ON SYSTEM RELIABILITY AND SAFETY, ICSRS

参与作者：恩里克.齐奥

通讯作者：何宇轩

第一作者：苏怀,林益帆,张劲军

论文类型：会议论文

论文概要：何宇轩,苏怀,恩里克.齐奥,林益帆,张劲军，A Physics-Informed Training Approach for Data-Driven Method in Remaining Useful Life Estimation，2022 6TH INTERNATIONAL CONFERENCE ON SYSTEM RELIABILITY AND SAFETY, ICSRS，2023，500-504

论文题目：A Physics-Informed Training Approach for Data-Driven Method in Remaining Useful Life Estimation