工业装备健康分析是对传统的工业装备故障诊断的扩展，是信息科学的前沿研究领域。针对工业装备物理/数学模型难以获取，经验知识缺少等难题，本项目拟从多源工业大数据融合方法、健康状态评估及寿命预测等三方面，系统深入地研究求解4类数据（传感器实时数据、历史数据、相关软数据及“跨界”数据）的状态特征向量的生成方法，提出基于距离相似度计算的可测性健康分析方法和变工况条件下基于实时健康值的拟合度计算剩余使用寿命预测方法。以多源大数据融合为基础，着力解决如何建立与求解问题相匹配的健康状态综合评估指标体系这一关键问题。通过融合4类数据，以及对所构建的模型在典型的装备系统中进行测试、验证和示范应用，为工业装备健康管理提供新的分析方法和工具，同时也为信息科学探索实现智能运维提供新的理论及技术思路。

Industrial equipment health analysis is an extension of the traditional fault diagnosis, which is the frontier research field of information science. Aiming at the problem of difficult to obtain the physical or mathematical model of industrial equipment and lack of empirical knowledge, this project will systematically and deeply investigate the generation of four classes of data-oriented (real-time data of sensors, historical data, related soft data and "cross-border" data )status feature vector from three aspects: fusion of multi-source and large industry data, assessment of health status and life prediction, to propose the construction idea of distance similarity calculation based measurable health analysis model and the calculation method of residual life based on fitting degree of real-time health value under variable working conditions is investigated. On the basis of the investigation of the fusion of multi-source and large data, this project focuses on how to establish the index system of health status assessment suiting for the problems to solve. Through proposing the fusion method of the four classes data and the testing, verification and application demonstration for the built models. This project will provide a new analysis technique and a new tool for the applications of industrial equipment health management, and also offer a new thought and approach for exploring intelligent operation and maintenance in information science.