We are now at a point where airframes, aeroengines and their integration require radical changes to meet increasingly aggressive environmental targets. Generally, fan noise emitted from rotor-stator assemblies would be one of the dominant noise sources for modern and next-generation aircraft engines, which shall direct most research interest into fans in the coming decade and, therefore, the associated measurement constitutes the main focus of the current review. Amongst various approaches, an experimental study of fan noise is usually efficient and of high-fidelity, but the associated cost is expensive. Moreover, the design of a test would be time-consuming due to the adequate choice of testing methods with the adaption to the testing case and rig. In addition, in most cases the experimental setup requires some sort of optimization to achieve high measurement accuracy. This article provides a contemporary review of the most well-known and state-of-the-art testing methods with the focus on fan noise problems. More specifically, the acoustic mode detection and noise source reconstruction methods are most relevant to the understanding of noise generation mechanism and propagating characteristics, which are useful for further noise reduction studies, and therefore are extensively reviewed in this article. As timely guidance to potential interested readers, this paper also provides an overview of recent developments based on the compressive sensing and machine learning techniques that have enabled disruptive innovations by fundamentally changing the testing practices of conventional measurement methods, thus constituting the continued research direction for next-generation aeroengines.

现在，我们需要对机体，航空发动机及其集成进行彻底的更改，以实现日益严格的环境目标。通常，转子-定子组件发出的风扇噪声将是现代和下一代飞机发动机的主要噪声源之一，这将在未来十年中将大多数研究兴趣引向风扇，因此，相关的测量构成了主要关注点。当前评论的。在各种方法中，对风扇噪声的实验研究通常是高效且高保真的，但相关的成本却很高。而且，由于要根据测试用例和设备进行适当的选择，因此测试的设计将很耗时。此外，在大多数情况下，实验装置需要某种优化以实现高测量精度。本文提供了有关最著名和最先进的测试方法的当代评论，重点是风扇噪声问题。更具体地说，声学模式检测和噪声源重构方法与对噪声产生机理和传播特性的理解最相关，这对于进一步的降噪研究很有用，因此本文对此进行了广泛的综述。作为对潜在感兴趣的读者的及时指导，本文还概述了基于压缩感测和机器学习技术的最新发展，这些技术通过从根本上改变传统测量方法的测试方法，实现了颠覆性创新，