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振动数据中本征模的识别

Identification of Eigenmodes in Vibration Data
课程网址: http://videolectures.net/mla09_preisach_ioeivd/  
主讲教师: Christine Preisach
开课单位: 希尔德斯海姆大学
开课时间: 2009-07-20
课程语种: 英语
中文简介:
振动是系统对内部或外部刺激的响应,导致振荡。如果系统以与所谓的本征模相同的频率激励,则振动会引起动态应力,这会损坏系统[2]。因此,振动数据中的本征模的识别是航空航天工业中的重要问题,例如,喷气发动机在投入使用前需要进行认证,并且必须检测到任何危险的振动。通常手动分析这些数据,因为这是一个耗时的过程,可以应用机器学习以支持工程师的工作。振动数据通常可视化为2D图像(坎贝尔图),并且本征模式显示为线。    我们引入了一种迭代算法,使用背景知识来识别本征模。我们的算法扩展了原始的霍夫变换[3,1],这是一种用于检测线条和其他可变形状的图像处理算法。最后,我们在评估中表明,我们用于识别本征模式的方法,应用于由欧洲主要喷气发动机制造商提供的数据集,优于有限元模型的预测,并且使用实验室测量对基础模型具有竞争力。
课程简介: Vibration is the response of a system to an internal or external stimulus causing it to oscillate. Vibration causes dynamic stress if the system is excited at the same frequency as the so called Eigenmodes and this can damage the system [2]. Thus, the identi cation of Eigenmodes in vibration data is an important issue in the aerospace industry, e.g. jet engines need to be certi ed before going into service and any dangerous vibration has to be detected. This data is usually analyzed manually, since this a time consuming process, machine learning can be applied in order to support engineers in their work. The vibration data is usually visualised as 2D images (campbell plots) and the Eigenmodes are displayed as lines. We introduce an iterative algorithm using background knowledge for the identi cation of Eigenmodes. Our algorithms extends the original Hough Transform [3, 1], an image processing algorithm used for detection of lines and other parametrisable shapes. Finally we show in our evaluation that our approach for identifying Eigenmodes, applied on a data set provided by a major European jet engine manufacturer, outperforms the prediction of the Finite Element Model and is competitive to the base model using lab measurements.
关 键 词: 振动; 动态应力; 本征模识别
课程来源: 视频讲座网
最后编审: 2019-06-28:cjy
阅读次数: 80