Wireless Intelligent Sensing Devices

Structural health monitoring is the process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure. Under this context, feature extraction is the process of identifying damage-sensitive information from measured data. Feature extraction is an essential component of a SHM system needed to convert raw sensor data into useful information about the structural health condition. The need for robust health monitoring and prognosis of components in remote or difficult to access locations is driving the advancement of sensing hardware and processing algorithms. The Wireless Intelligent Sensing Devices (WISD) research project aims to attend to this need. In this document a feature extraction algorithm, referred to as soft computing feature extraction algorithm, is developed as part of the WISD project to extract damage-sensitive information from measured response data of tie bar and bearing system of pitch link components of the main rotor hub of a Lynx Helicopter. The feature extraction algorithm is based on a combining of discrete wavelet transform theory and fuzzy logic theory. The results of applying the proposed feature extraction approach to tie bar and pitch link data are presented. Additionally, methods for pattern recognition and critical degradation detection of tie bar and critically worn detection of pitch link are proposed. Results show that the proposed algorithms are capable of detecting critical degradation of tie bar and have the ability to discriminate between unworn (undamaged) and worn (damaged) pitch link bearings.