Meta-modeling of heterogeneous data streams: A dual-network approach for online personalized fault prognostics of equipment. (3rd July 2022)

• Record Type: Journal Article
• Authors: Yu, Hongtao; Hua, Zhongsheng
• Is Part Of: IISE transactions. Volume 54:Number 7(2022)
• Page Start: 672
• Page End: 685
• Publication Date: 2022-07-03
• Subjects: Individual variability -- fault prognostics -- meta-learning -- neural networks; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: Https://www.tandfonline.com/doi/10.1080/24725854.2021.1918804
• Languages: English
• ISSNs: 2472-5854

Abstract

Abstract: 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.