风电齿轮箱损伤检测的多分辨率动态模态分解

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Paolo Climaco, J. Garcke, Rodrigo Iza-Teran
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引用次数: 2

摘要

摘要介绍了一种基于多分辨率动态模式分解(mrDMD)对传感器数据进行分析的齿轮箱损伤检测方法。应用重点是在不同负载条件下,特别是不规则和随机的风波动下,对风力涡轮机齿轮箱进行状态监测。我们分析了一个简单非线性齿轮箱模型在健康和受损情况下以及在不同风况下的模拟振动响应数据。将mrDMD应用于传感器数据的延时快照,我们可以提取这些振动信号中突出与损伤相关特征的分量,并能够识别损伤。与傅立叶分析、时间同步平均和经验模式分解的比较表明了所提出的基于mrDMD的数据分析方法在损伤检测中的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-resolution dynamic mode decomposition for damage detection in wind turbine gearboxes
Abstract We introduce an approach for damage detection in gearboxes based on the analysis of sensor data with the multi-resolution dynamic mode decomposition (mrDMD). The application focus is the condition monitoring of wind turbine gearboxes under varying load conditions, in particular irregular and stochastic wind fluctuations. We analyze data stemming from a simulated vibration response of a simple nonlinear gearbox model in a healthy and damaged scenario and under different wind conditions. With mrDMD applied on time-delay snapshots of the sensor data, we can extract components in these vibration signals that highlight features related to damage and enable its identification. A comparison with Fourier analysis, time synchronous averaging, and empirical mode decomposition shows the advantages of the proposed mrDMD-based data analysis approach for damage detection.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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