数据驱动的复杂结构动态系统故障预测与诊断

项目名称： 数据驱动的复杂结构动态系统故障预测与诊断

项目编号： No.61473222

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 胡绍林

作者单位： 西安理工大学

项目金额： 85万元

中文摘要： 在轨航天器属于典型的复杂结构动态系统。近年来国际国内航天故障频繁，急需多途径探索航天故障预测与诊断技术。本项目以航天器在轨过程故障预测与诊断技术发展的3大薄弱环节为研究对象，采用数据驱动与问题驱动结合的方式，开展遥测数据挖掘与形态建模、异变过程关联分析、异变检测与故障诊断、趋势分析与长程预报、故障预测与健康管理等彼此衔接的5个方面技术研究，解决带杂质强干扰含异变非平稳数据的形态聚类与数学建模、遥测数据异变与系统部件故障多对多强耦合情形下的故障定位与故障预测、类间歇过程长程预报与跨周期异变预警等3方面关键科学问题，建立一套集数据挖掘、过程监控、故障诊断、故障预测和健康管理等多功能于一体的技术架构与软件平台,并在遥测数据形态聚类、多通道异变关联分析、多故障定位与故障预测等方面取得显著创新,有效拓展复杂结构动态系统故障预测与诊断技术的发展空间，并为航天器在轨安全运行提供有力的技术支持。

中文关键词： 故障诊断与预测；过程监控；数据驱动；数据挖掘；故障预测

英文摘要： The on-orbit spacecraft is a tripcal dynamic system with complicated structure. Recently,there were a lot of faults and failures which took place in the international fields of spaceflighting engineering.It is very obligatory to explore some new techniques of faults prognostics/diagnostics by means of expansive approaches.In this proposal application, the researches aim at the three weakness aspects in the techniques fields of faults prognostics/ diagnosis for on-orbit spacecraft. Relying on the combination of the data-driven mode with the problems-driven mode, five topics of the main research contents will be explored and be solved, which include the following techniques of spaceflighting data mining and modality modlling, the analysis of associate relevancy in different changes, changes detection and faults diagnosis, tredency analysis and the long-term predicting algorithms of telemetry data, faults prognostics and health management of the on-orbit spacecraft,etc.In our research, three key scientific problems will be solved, which are the problem of modality classification and modelling telemetry non-stationary data series with outliers,strong noise and changes,the problem of location inference and forecast of faults by means of the changes information of telemetry in the case that the changes of telemetry data are mapped into faults of components in spacecraft with strong compuling relationship,and the problem of long-term preditcion and across-cycle change forecast of telemetry data series from the similar intermission process.At the end of our research, a technical frame and software platform will be built to integrate many kinds of functions, such as data mining, processes monitoring,faults diagnosis, faults prognostics and health management,etc. In our research,a series of technique innovation will be created in the following aspects: modality classification of telemetry data series, associate relevancy analysis of multidimensional telemetry data changes, multi-faults location and prognostics,etc. It is sure that these scientific researches are helpful for us to expand the fields of diagnosis & prognostics of faults in the dynamic system with complex structure and to increase the safety of spacecrafting engineering.

英文关键词： Fault Diagnosis and Prognostics;Process Monitoring;Data Driven;Data Mining;Fault Prognostics