Soft Computing Feature Extraction for Health Monitoring of Rotorcraft Structures

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Abstract
Structural health monitoring (SHM) 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. In this paper a feature extraction algorithm, referred to as soft computing feature extraction algorithm, is developed to extract damage-sensitive information from measured response data of helicopter rotor-head components. The proposed feature extraction algorithm is based on a combination of discrete wavelet transform theory and fuzzy logic theory. The results of applying the proposed feature extraction approach to tie bar data are presented. Results show that the proposed algorithm is capable of extracting features sensitive to the degradation of tie bar systems.

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