With increasing mobile instrument inspection systems like mobile inspection cameras applied in modern industrial automation lines, their time-varying modal parameters need to be thoroughly studied since they are operating under various random vibration excitations which are mostly unpredictable and unrepeatable. This paper investigates modal parameter identification of time-varying mobile mechanical systems through a poly-reference expansion based on output-only method in time-frequency domain. The poly-reference parametric model is presented through a time-frequency-domain right matrix fraction description. Based on the poly-reference parametric model, a poly-reference least square estimator and a poly-reference maximum likelihood estimator of modal parameters of time-varying systems are developed. For the case of only one batch of measurements, a novel approach to estimate the covariance matrix for the poly-reference maximum likelihood estimator by non-repeatable measurement is also proposed in this paper. A numerical example demonstrates the advantageous performance of the proposed estimators, such as the estimation of the close-coupled modes. Finally, a laboratory experiment validates these proposed estimators via a comparative way.

随着现代工业自动化生产线中使用的移动式仪器检查系统（例如移动式检查摄像机）的不断增加，由于它们的时变模态参数在各种随机振动激励下运行，而这些随机振动通常是无法预测和不可重复的，因此需要对其进行彻底研究。本文研究基于时频域仅输出法的多参考扩展时变移动机械系统的模态参数辨识。通过时频域右矩阵分数描述给出了多参考参数模型。基于多参考参数模型，开发了时变系统模态参数的多参考最小二乘估计器和多参考最大似然估计器。对于仅一批测量的情况，本文还提出了一种通过不可重复测量来估计多参考最大似然估计器协方差矩阵的新方法。数值示例证明了所提出的估计器的优越性能，例如对紧密耦合模式的估计。最后，实验室实验通过比较的方式验证了这些建议的估计量。