Ambient vibration is an unknown excitation source that may produce stationary or non-stationary signals. Under such circumstances, traditional feature extraction techniques may not yield relevant features to damage and not provide reliable results of damage identification. The main objective of this article is to propose a data-driven method based on the concept of statistical pattern recognition for locating damage under ambient vibration and unpredictable signal nature in terms of simultaneously stationary and non-stationary behavior. This method is generally comprised of a three-level hybrid algorithm for feature extraction and new statistical distance metrics for feature analysis. The proposed feature extraction method aims at providing new damage-sensitive features in three levels including (1) analyzing the nature of measured vibration signals in terms of stationarity or non-stationarity, and normalizing non-stationary signals by detrending and differencing techniques, (2) modeling each vibration signal by an Autoregressive Moving Average (ARMA) model along with extracting the model residuals, and (3) estimating the power spectral density of residual samples as a new spectral-based feature. To identify the location of damage via spectral-based features, this article proposes two new spectral-based measures called Jeffery’s and Smith’s distances. The major contributions of this study include proposing a new feature extraction method for dealing with the problem of unpredictable vibration nature and introducing two new distance metrics for damage identification. Experimental vibration measurements of a well-known laboratory structure are utilized to verify the proposed methods. Results demonstrate that these approaches succeed in accurately extracting relevant features and locating damage under ambient vibration and unpredictable signal nature.

环境振动是一种未知的激励源，可能产生平稳或非平稳信号。在这种情况下，传统的特征提取技术可能无法产生与损伤相关的特征，也无法提供可靠的损伤识别结果。本文的主要目的是提出一种基于统计模式识别概念的数据驱动方法，用于在环境振动和不可预测的信号性质下，同时根据静止和非静止行为定位损伤。该方法通常由用于特征提取的三级混合算法和用于特征分析的新统计距离度量组成。所提出的特征提取方法旨在提供三个层次的新的损伤敏感特征，包括（1）从平稳或非平稳方面分析测量振动信号的性质，并通过去趋势和差分技术对非平稳信号进行归一化，（2） ) 通过自回归移动平均 (ARMA) 模型对每个振动信号进行建模并提取模型残差，以及 (3) 将残差样本的功率谱密度估计为新的基于频谱的特征。为了通过基于光谱的特征识别损坏的位置，本文提出了两种新的基于光谱的度量，称为 Jeffery 距离和 Smith 距离。这项研究的主要贡献包括提出一种新的特征提取方法来处理不可预测的振动性质问题，并引入两种新的距离度量来进行损伤识别。众所周知的实验室结构的实验振动测量被用来验证所提出的方法。结果表明，这些方法成功地在环境振动和不可预测的信号性质下准确提取相关特征并定位损坏。