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Vibration-based Damage Assessment for Controller Reconfiguration: Application to an Oilpan

机译:控制器重新配置的基于振动的损伤评估:应用于油底壳的应用

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摘要

One of the objectives of the EU research project InMAR ("Intelligent Materials for Active Noise Reduction") is to reduce car engine noise by active control. An oilpan of a passenger car serves as a demonstrator. A concern in the application of active control is that the controlled system may change during service life (e.g. due to damage), and hence, may degrade the control performance. This paper presents two vibration-based methods that are able to autonomously detect damage and yield updated experimental models of the structure. A first approach is based on (operational) modal analysis. Based on vibration measurements, the modal parameters of the structure are estimated. The idea is now to automate this process so that, without human intervention, a representative dynamic model of the structure is always available. A second approach uses multiple-model estimation in the case when the state-space models have different state dimensions. To this end, an existing non-interacting multiple-model estimator has been extended to make it alert to jumps from one model to another. Both techniques ("Automatic Modal Analysis" and "Alert Autonomous Multiple Model Estimator") will be applied to experimental vibration data from an oilpan of a passenger car subjected to damage (loosening of bolts).
机译:欧盟研究项目Inmar的一个目标之一(“用于有源降噪”的智能材料)是通过主动控制降低汽车发动机噪声。乘用车的油本作为演示者。在应用主动控制的应用中的一个问题是受控系统可以在使用寿命期间改变(例如由于损坏),因此可能降低控制性能。本文介绍了两种基于振动的方法,可以自主检测损坏和产量更新的结构实验模型。第一方法是基于(操作)模态分析。基于振动测量,估计结构的模态参数。目前现在可以自动化此过程,使得没有人为干预,始终可用的结构的代表性动态模型。当状态空间模型具有不同的状态维数时,第二种方法使用多模型估计。为此,已经扩展了现有的非交互多模型估计器以使其警报从一个模型跳转到另一个模型。这两种技术(“自动模态分析”和“警报自主多模型估计器”)将应用于经过损坏的乘用车(螺栓的松动)的乘用车的实验振动数据。

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