TY - GEN
T1 - Soft computing signal processing for health monitoring of tie-bar of rotor head structure
AU - Escamilla-Ambrosio, P. J.
AU - Lieven, N.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=51349089134&partnerID=8YFLogxK
U2 - 10.1109/ISSNIP.2007.4496836
DO - 10.1109/ISSNIP.2007.4496836
M3 - Contribución a la conferencia
AN - SCOPUS:51349089134
SN - 1424415020
SN - 9781424415021
T3 - Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP
SP - 155
EP - 160
BT - Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP
T2 - 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP
Y2 - 3 December 2007 through 6 December 2007
ER -
