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OPAX: A new transfer path analysis method based on parametric load models

机译:OPAX:一种基于参数负载模型的新传输路径分析方法

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摘要

Since its first publication in the beginning of the 1980s, transfer path analysis (TPA) has evolved into a widely used tool for noise and vibration troubleshooting and internal load estimation, for single source and multivariate problems. One of the main bottlenecks preventing its even more widespread use in the vehicle development process is the test time needed to build the full data model, requiring not only in-operation tests but also extensive frequency response function (FRF) measurements. As a consequence, several new approaches, such as operational TPA, have appeared over the past years attempting to circumvent this limitation. These methods attract quite some attention as they only require operational data measured at the path references and target locations. However, despite being time-efficient, these methods suffer from several limitations that can lead to incorrect path contribution interpretations and wrong engineering decisions. Hence, a new TPA approach is proposed, providing a good compromise between path accuracy and measurement time. The method is referred to as OPAX as it essentially uses in-operation data complemented with a minimal set of extra tests with forced excitation. The key idea of OPAX is the use of parametric models for identifying the operational loads. This makes the method scalable, enabling the engineer to use a simple model based on a small amount of measurement data for quick troubleshooting or increase accuracy using a more complex model together with additional measurements.
机译:自1980年代初首次发布以来,传递路径分析(TPA)已经发展成为一种广泛用于噪声和振动故障排除以及内部负荷估算的工具,用于解决单源和多变量问题。阻止其在车辆开发过程中更广泛使用的主要瓶颈之一是建立完整数据模型所需的测试时间,这不仅需要运行中的测试,还需要广泛的频率响应功能(FRF)测量。结果,在过去几年中出现了几种新的方法,例如可操作的TPA,试图规避此限制。这些方法吸引了相当多的注意力,因为它们仅需要在路径参考和目标位置处测量的操作数据。但是,尽管这些方法省时高效,但仍存在一些局限性,可能导致错误的路径贡献解释和错误的工程决策。因此,提出了一种新的TPA方法,在路径精度和测量时间之间提供了一个很好的折衷方案。该方法之所以称为OPAX,是因为它主要使用运行中的数据并辅以最少的一组带有强制激励的额外测试。 OPAX的主要思想是使用参数模型来识别操作负载。这使该方法具有可扩展性,使工程师能够使用基于少量测量数据的简单模型进行快速故障排除,或者使用更复杂的模型以及其他测量来提高准确性。

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