Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks  

作　　者：Xiang Li Yixiao Xu Naipeng Li Bin Yang Yaguo Lei 

机构地区：[1]IEEE [2]the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System,Xi’an Jiaotong University,Xi’an 710049,China [3]Student Member [4]Senior Member

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第1期121-134,共14页自动化学报（英文版）

基　　金：supported by the National Science Fund for Distinguished Young Scholars of China(52025056);Fundamental Research Funds for the Central Universities(xzy012022062)。

摘　　要：In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.

关 键 词：Adversarial training data fusion deep learning remaining useful life(RUL)prediction sensor malfunction 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]