Abstract Time-varying systems are applied extensively in practical applications, and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems. To improve the identification accuracy for time-varying systems, this study puts forward a novel parameter identification approach in the time–frequency domain using intrinsic chirp component decomposition (ICCD). ICCD is a powerful tool for signal decomposition and parameter extraction, with good signal reconstruction capability in a high-noise environment. To maintain good identification effects for the time-varying system in a noisy environment, the proposed method introduces a redundant Fourier model for the non-stationary signal, including instantaneous frequency (IF) and instantaneous amplitude (IA). The accuracy and effectiveness of the proposed approach are demonstrated by a single-degree-of-freedom system with three types of time-varying parameters, as well as an example of a multi-degree-of-freedom system. The effects of different levels of measured noise on the identified results are also discussed in detail. Numerical results show that the proposed method is very effective in tracking the smooth, periodical, and non-smooth variations of time-varying systems over the entire identification time period even when the response signal is contaminated by intense noise. Graphic abstract

中文翻译：

---

使用固有啁啾分量分解从自由振动中参数识别时变系统

摘要 时变系统在实际应用中应用广泛，其相关参数辨识技术对时变系统结构健康监测具有重要意义。为了提高时变系统的识别精度，本研究提出了一种使用固有线性调频分量分解（ICCD）的时频域参数识别方法。ICCD是信号分解和参数提取的有力工具，在高噪声环境下具有良好的信号重构能力。为了在噪声环境中对时变系统保持良好的识别效果，所提出的方法引入了非平稳信号的冗余傅立叶模型，包括瞬时频率（IF）和瞬时幅度（IA）。通过具有三种时变参数的单自由度系统以及多自由度系统的示例证明了所提出方法的准确性和有效性。还详细讨论了不同水平的测量噪声对识别结果的影响。数值结果表明，即使响应信号受到强烈噪声的污染，所提出的方法在整个识别时间段内跟踪时变系统的平滑、周期性和非平滑变化方面也非常有效。图形摘要 还详细讨论了不同水平的测量噪声对识别结果的影响。数值结果表明，即使响应信号受到强烈噪声的污染，所提出的方法在整个识别时间段内跟踪时变系统的平滑、周期性和非平滑变化方面也非常有效。图形摘要 还详细讨论了不同水平的测量噪声对识别结果的影响。数值结果表明，即使响应信号受到强烈噪声的污染，所提出的方法在整个识别时间段内跟踪时变系统的平滑、周期性和非平滑变化方面也非常有效。图形摘要