Soft Computing Signal Processing for Health Monitoring of Tie-Bar of Rotor Head Structure

The need for robust health monitoring and prognostics of structural components in remote or difficult-to-access locations, e.g. helicopter rotor-head structure, is driving the advancement of wireless intelligent sensor devices (WISD). Damage detection techniques, combined with advanced signal processing, are the core components of a structural health monitoring (SHM) system. In this context, feature extraction is an essential component of a SHM system that converts raw sensor data into useful information about the structure health condition. The level of signal processing that can be performed in a WISD depends on the capability of the processing element in terms of speed, memory and energy consumption. But the real bottleneck for energy efficiency is the fact that communications dominate the WISD energy consumption. Therefore, running intelligent local data interrogation algorithms on-board the WISD is a mechanism through which considerable battery power can be preserved. In that sense, in this paper a soft histogram feature extraction algorithm is developed to extract damage-sensitive information from measured response data of tie-bar component of the main rotor hub of a Lynx helicopter. In addition, a method for pattern recognition and critical degradation detection of tie-bar is proposed based on the extracted features and a combination of statistical process control and fuzzy sets theory. Results show the applicability of the proposed approaches.