Structural freeplay due to loosened mechanical linkages is a discrete nonlinear event which occurs pseudo-routinely in modern aircraft, causing severe airframe vibration. This impacts fatigue life, and has serious implications for fleet management and Structural Health Monitoring (SHM). While the concepts which drive SHM for aircraft are traditionally based on reactive procedures, we are currently observing a major shift towards actionable and pro-active condition-based maintenance, known as Prognostics and Health Management (PHM), to significantly reduce fleet sustainment costs. Given this current paradigm shift, there is a demand for the development of novel strategies to address decades old SHM problems, where the adaptation of existing methods or the development of new and innovative techniques both play critical roles. In this paper a signal processing framework is presented, based upon well-established nonlinear system identification methods, to rapidly diagnose structural freeplay in aircraft systems with a focus on the requirements of PHM technology. The framework exploits the nonlinear dynamical characteristics of the structural freeplay anomaly in a transonic aeroelastic system by specifically targeting rich bilinear signatures that are encoded in time-domain sensory outputs, via the Higher-Order Spectra (HOS) and the Empirical Mode Decomposition (EMD). The characteristic freeplay signatures which were initially extracted from computational transonic aeroelastic models are shown to be analogous in a transonic flight-test case-study (an all-movable horizontal tail with actuator freeplay), presenting a rare and important opportunity to verify the practical freeplay identification research. Once verified, a comprehensive understanding of the fundamental bilinear signatures allows the HOS and EMD to be adapted and refined towards a structured freeplay diagnosis framework. Using the extensive flight-test dataset as a case study, it is shown that the freeplay location and magnitude information can be extracted with a high level of robustness, verified by making consistent predictions over a period of three years and several maintenance cycles, with a large variation in Mach number and angle-of-attack (predominantly high angle maneuvers). The paper is intended to communicate the fundamental principles and significance of the data-driven framework, highlighting revisiting and adapting existing well-established nonlinear identification tools, it is possible to address the requirements of contemporary SHM, although practical implementation requires ongoing research. Limitations of the data-driven approach are discussed, predominantly related to data acquisition requirements.

由于松动的机械连杆引起的结构自由游隙是一种离散的非线性事件，它在现代飞机中伪常规地发生，导致严重的机身振动。这会影响疲劳寿命，并对车队管理和结构健康监测 (SHM) 产生严重影响。虽然推动飞机 SHM 的概念传统上基于反应程序，但我们目前正在观察到向可操作和主动的基于条件的维护（称为预测和健康管理 (PHM)）的重大转变，以显着降低机队维护成本。鉴于当前的这种范式转变，需要开发新的策略来解决几十年前的 SHM 问题，其中现有方法的适应或新的和创新技术的开发都起着关键作用。在本文中，基于成熟的非线性系统识别方法，提出了一种信号处理框架，以快速诊断飞机系统中的结构自由游隙，重点关注 PHM 技术的要求。该框架通过高阶谱 (HOS) 和经验模式分解 (EMD)，专门针对在时域传感输出中编码的丰富双线性特征，从而利用跨音速气动弹性系统中结构自由运动异常的非线性动力学特性. 最初从计算跨音速气动弹性模型中提取的特征自由运动特征与跨音速飞行测试案例研究（具有致动器自由运动的全可移动水平尾翼）类似，提供了一个难得的重要机会来验证实际的自由游戏识别研究。一旦经过验证，对基本双线性特征的全面理解使 HOS 和 EMD 能够针对结构化的自由播放诊断框架进行调整和完善。使用广泛的飞行测试数据集作为案例研究，表明可以高度稳健地提取自由运行位置和幅度信息，通过在三年期间和几个维护周期内做出一致预测来验证，具有马赫数和迎角的巨大变化（主要是高角度机动）。本文旨在传达数据驱动框架的基本原则和意义，强调重新审视和适应现有的完善的非线性识别工具，可以满足当代 SHM 的要求，尽管实际实施需要持续研究。讨论了数据驱动方法的局限性，主要与数据采集要求有关。