Environmental Effect Removal Based Structural Health Monitoring in the Internet of Things

Abstract

Internet of things (IoT) has provided a promising choice for structural health monitoring (SHM) recently. In vibration-based structural health monitoring, structural features which are extracted from periodically spaced measurement using sensors are subject to variable environmental conditions. This paper proposes an environmental effect removal based structural health monitoring scheme in an IoT environment. Principal component analysis (PCA) is employed to eliminate environment effects from sensor data which contain both real vibration feature of architectural structure and environmental interferences. After environmental effects removal, Hilbert-Huang transformation (HHT) which is a classical method for signal analysis is used for structural health analysis and monitoring. Simulation results show that the proposed scheme achieves high accuracy in structural health monitoring and robust performance against environmental interferences.