面向不平衡样本的流形学习故障诊断方法

项目名称： 面向不平衡样本的流形学习故障诊断方法

项目编号： No.61273164

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 冯健

作者单位： 东北大学

项目金额： 80万元

中文摘要： 复杂设备及生产过程的样本数据呈现的严重不平衡特性对故障诊断的关键环节-故障特征的提取造成了很大的困难，面向不平衡样本的故障诊断研究是一个具有重要理论意义和很高工程实用价值的科学问题。项目以典型工业系统为对象，在深入分析数据不平衡特性的不完整、强噪声、相对冗余和样本不均四种表现的基础上，针对复杂非线性故障的特征提取问题，研究以半监督和流形学习方法为核心的故障诊断理论与方法。研究内容包括：面向不平衡样本的半监督数据重构方法；基于DAPK流形学习建模技术；基于代价敏感小波网络的故障特征提取方法。研究的显著特点是：1)提出了基于重采样和半监督学习的数据重构方法，减轻了因故障样本稀少对流形结构产生的扭曲影响，解决了数据不平衡对特征提取带来的困难；2)提出了具有先验导向的局部优化和整体排列流形学习方法挖掘样本数据蕴含的结构信息和几何规律，解决了故障特征重叠问题，提高了故障诊断的早期预报能力和准确率。

中文关键词： 数据驱动；故障诊断；样本不平衡；特征提取；流形学习

英文摘要： The serious imbalance characteristic of sample data leads to great troubles for fault feature extraction which is one of the key issues of fault diagnosis in the complex equipment and production process. The fault diagnosis for imbalanced samples is a scientific problem which has important theoretical significance and high engineering practical value. The project is to study the semi-supervised and manifold learning method for the complex nonlinear fault feature extraction problem as the core of the theories and methods for fault diagnosis, which is on the basis of an in-depth analysis for the four performances of imbalanced data characteristics: not complete, strong noise, the relative redundancy and samples unequal in a typical industrial system as an object. The research content includes: the semi-supervised data reconstruction methods for imbalance samples; the modeling technology based on DAPK manifold learning; the feature extraction method based on cost-sensitive wavelet network. The research is distinguished as: 1) the data reconstruction method based on resample and semi-supervised learning is proposed. The method reduces the distortion influence to manifold structure, induced by the sparse fault samples. The difficulty, caused by data imbalance for feature extraction, can be solved. 2) the manifold lea

英文关键词： Data-driven；fault diagnosis；sample imbalance；feature extraction；manifold learning