Feature extraction and classification are crucial steps of a data-driven structural health monitoring strategy. One of the major issues in feature extraction is to extract damage-sensitive features from nonstationary signals under unknown ambient vibration. Furthermore, the use of high-dimensional features in damage detection is the other challenging issue, which may make a difficult and time-consuming process. This article is initially intended to propose a hybrid algorithm as a combination of EEMD technique and ARARX model for feature extraction. Subsequently, correlation-based dynamic time warping method is proposed to detect damage by using randomly high-dimensional multivariate features. Due to the importance of damage localization, dynamic time warping is eventually applied to locate damage. Experimental datasets of the IASC-ASCE benchmark structure are utilized to validate the accuracy of proposed methods. Results suggest that the proposed methods are effective tools for damage detection and localization under ambient vibration and non-stationary and/or stationary signals

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