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

Vibration data analysis is an applicable approach to Structural Health Monitoring (SHM) using statistical pattern recognition. The objective of the paper is to identify the location of damage by a new feature extraction technique and propose some 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 a 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 the coefficient of variation, Fisher criterion, Fano factor formulated by using the features extracted from the PCA-convolution algorithm. To localize damage, the sensor location with the distance value exceeded from a 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 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 building model and a benchmark beam. Results demonstrate that the approaches presented here can influentially identify the location of damage by using the features extracted from the proposed PCA.