Identifying damage location under statistical pattern recognition by new feature extraction and feature analysis methods.

The main objective of this article is to identify the location of damage by a new feature extraction technique and to propose some efficient feature analysis tools as statistical distance measures. The proposed algorithm of feature extraction relies on a combination of the well-known Principal Component Analysis (PCA) and convolution strategy. After extracting the features from raw vibration signals of undamaged and damaged conditions, those are applied to the proposed feature analysis approaches called coefficient of variation, Fisher criterion, Fano factor, and relative reliability index, all of which are formulated by using statistical moments of the features extracted from the PCA-convolution algorithm. To localize damage, the sensor location with the distance value exceeding a certain threshold limit is identified as the damaged area. The main innovations of this research are to present a new hybrid technique of feature extraction suitable for Structural Health Monitoring (SHM) applications and four effective statistical measures for feature analysis and damage identification. The performance and reliability of the proposed methods are verified by a four-story shear-building model and a benchmark concrete beam. Results demonstrate that the approaches presented here can substantially identify the location of damage using the features extracted from the proposed PCA-convolution algorithm. [ABSTRACT FROM AUTHOR]