基于流形学习的风电系统传动部件多故障诊断及退化状态识别

项目名称： 基于流形学习的风电系统传动部件多故障诊断及退化状态识别

项目编号： No.61304104

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 赵玲

作者单位： 重庆交通大学

项目金额： 25万元

中文摘要： 风电系统传动部件的复杂化使得多重并发故障发生的概率增大。多故障并发时，不同故障特征相互混杂呈现出多耦合的复杂征兆，并非多个单故障的简单线性叠加，给故障诊断造成困难。本项目以风电系统关键传动部件为研究对象，以数据结构分析为基础，通过加入去相关约束条件，将信号经历高维重构、低维映射以及等距空间转换，实现故障特征解耦。考虑不同种类故障信号的频带、能量、结构、形态等均不同的特点，采用多流形结构，联合多特征融合结果，经过有监督的多流形学习，实现特征提取，并在故障演化机理分析的基础之上构建HSMM模型辨识风电系统关键传动部件的性能退化演变状态。 本项目采用基于数据的故障特征提取、退化状态识别技术实现主动的故障预测，在现有风电系统故障数据资料匮乏的条件下，有望对保持风电系统安全可靠性运行，预防故障提供一定的理论及技术支撑。

中文关键词： 风电系统；传动部件；特征提取；退化状态识别；

英文摘要： The complexities of structure of equipment and assembly technology of transmission components decide the high probability of concurrent faults condition. In general, concurrent faults is multi-coupled, which is not simple linear superposition of single failure and causing difficulties to the fault diagnosis. Based on analysis of data attributes combined with correlation constraints, this work does research on transmission components of wind power mainly by approach of high-dimensional reconstruction, low-dimensional mapping, and isometric space conversion to decouple the fault features. As there are difference of the frequency band, energy and structure of the different types of fault signal, supervised manifold learning and multi-feature fusion are combined to extract fault feature, and the HSMM model that can identify degradation state of transmission components is also built. With the application of data-based fault feature extraction, degradation state recognition technology initiative failure prediction, achievement of this work can contribute to provide a theoretical and technical support for maintaining the operational reliability of the wind power system security and preventing failure, in condition of scarcity of existing wind power system fault data.

英文关键词： wind power system；transmission components；feature extraction；degradation state recognition；