Meta-modeling of heterogeneous data streams: A dual-network approach for online personalized fault prognostics of equipment
针对设备退化过程的个体异质性导致预测模型可靠性下降的问题，提出一种双网络方法，结合循环神经网络和前馈神经网络在线预测剩余使用寿命，并通过元学习动态生成适配不同设备的网络。
In fault prognosis, the individual heterogeneity among degradation processes of equipment is a critical problem that decreases the reliability and stability of prognostic models. The presence of the diversity of degradation mechanisms, along with the complex temporal nature of multivariate measurements of equipment, make the existing approaches difficult to forecast the trend of health status and predict the Remaining Useful Life (RUL) of equipment. To resolve this problem, this article proposes a dual-network approach for online RUL prediction. The proposed approach predicts the RUL by constructing a recurrent neural network (RNN) and a Feedforward Neural Network (FNN) from the degradation measurements and failure occurrence data of equipment. The RNN is used to predict the evolution of degradation measurements, whereas the FNN is used to determine the failure occurrence based on the predicted measurements. Considering the individual heterogeneity problem, a novel meta-learning procedure is proposed for network training. The main idea of the meta-learning approach is to train two network generators to capture the average behavior and variation of equipment degradation, and generate dual networks dynamically tailored to different equipment in the online RUL prediction process. Numerical studies on a simulation dataset and a real-world dataset are performed for performance evaluation.