大数据下数模联动的随机退化设备剩余寿命预测技术  

Data-model Interactive Remaining Useful Life Prediction Technologies for Stochastic Degrading Devices With Big Data

作　　者：李天梅 司小胜 刘翔 裴洪 LI Tian-Mei;SI Xiao-Sheng;LIU Xiang;PEI Hong(Zhi Jian Laboratory,Rocket Force University of Engineering,Xi'an 710025)

机构地区：[1]火箭军工程大学智剑实验室,西安710025

出　　处：《自动化学报》2022年第9期2119-2141,共23页Acta Automatica Sinica

基　　金：国家自然科学基金(62073336,61922089,61773386)资助。

摘　　要：面向大数据背景下随机退化设备剩余寿命(Remaining useful life,RUL)预测的现实需求,结合随机退化设备监测大数据特点及剩余寿命预测不确定性量化这一核心问题,深入分析了机理模型与数据混合驱动的剩余寿命预测技术、基于机器学习的剩余寿命预测技术、统计数据驱动的剩余寿命预测技术以及机器学习和统计数据驱动相结合的剩余寿命预测技术的基本研究思想和发展动态,剖析了当前研究存在的局限性和共性难题.针对存在的局限性和共性难题,以多源传感监测大数据下剩余寿命预测问题为例,提出了一种数模联动的大数据下随机退化设备剩余寿命预测解决思路,并通过航空发动机多源监测数据初步验证了该思路的可行性和有效性.最后,借鉴数模联动思路,综合考虑机器学习方法和统计数据驱动方法的优势,紧紧扭住大数据背景下随机退化设备剩余寿命预测不确定性量化问题,提出了大数据背景下深度学习与随机退化建模交互联动、监测大数据与剩余寿命及其预测不确定性映射机制、非理想大数据下的剩余寿命预测等亟待解决的关键科学问题.Focused on the realistic desire to the remaining useful life(RUL)prediction of stochastic degrading devices with big data,according to the characteristics of the big monitoring data of stochastic degrading devices and the core issue quantifying the uncertainty in the RUL prediction,this paper provides deep analysis of basic principles and advances of classical solution avenues to RUL prediction of stochastic degrading devices with big data from the data-driven viewpoint.The reviewed methods mainly include hybrid techniques based on physical model and data,machine learning method based techniques,statistical data-driven techniques,and the combination of machine learning methods and statistical data-driven methods.At the meanwhile,the limitations and common problems in existing studies are dissected.As for these limitations,taking the big monitoring data from multi-source sensors as an example,this paper presents a data-model interaction perspective to solve the RUL prediction problem for stochastic degrading devices with big data.The application to multi-source monitoring data of aero-engines preliminarily verifies the feasibility and effectiveness of this presented data-model interaction idea.Finally,inspired by the presented data-model interaction idea,it will be beneficial to tightly holding the main line of the RUL prediction uncertainty quantification by synthesizing advantages of intelligent methods and statistical data driven methods.As such,this paper discusses several key scientific issues for RUL prediction of stochastic degrading devices with big data,including the interactive collaboration idea between deep learning and stochastic degradation modeling,the mapping mechanism between the big monitoring data and the RUL with the prediction uncertainty quantification,RUL prediction issues under non-ideal data,etc.

关 键 词：大数据 剩余寿命预测 数模联动 深度学习 随机退化建模 

分 类 号：V328.2[航空宇航科学与技术—人机与环境工程] TP311.13[自动化与计算机技术—计算机软件与理论]