Structural health monitoring system of construction facilities: enhanced training approach

Abstract

Structural health monitoring (SHM) of constructions under operating conditions becomes a relevant problem. When dealing with this problem the specific nature and environment conditions of the controlled facility should be considered. Therefore it is very important to continuously train the monitoring system with new data acquired from sensors. Current paper presents an approach of considering training the SHM system as training of defect evolution detection model. This method consists of four subsequent stages which allows to build and train defect evolution detection model on the acoustic emission sensor data. It includes data preparation stages (feature extraction, feature selection), outlier detection and training stage via proposed modification of the One-Class SVM anomaly detection method. Proposed approach was verified on real fuel and energy infrastructure facility. Obtained results give good grounds for utilizing the suggested approach in SHM systems with acoustic emission sensors allowing detection evolutionary defects in controlled facility which contributes to prevention of emergency situations that may have economic, social and ecological consequences.