Enhancing vibration analysis by embedded sensor data validation technologies

Abstract

The ability to predict and understand the responses of an airframe with simultaneous external influences acting on its elements is a challenging effort. Moreover, although considerable research has been devoted to monitoring structures within the aerospace industry (commercial, military, and space), successful field implementations have not been widely achieved. Breakthroughs for in-flight measurement techniques and processing tools are thus required for enhancing flight research and to ultimately boost operations, increase safety, and reduce costs. This article presents an embedded approach based on a high performance vibration-based diagnostic framework using validated data from low power miniature smart sensors. The architecture is divided into two levels, with the low level built on smart sensors capable of self-diagnostics, robust data acquisition, and vibration analysis, and the high level comprising a computation system with a graphical user interface, feature extraction toolset, and artificial neural network diagnostics. The goal is a system consisting of smart sensors and intelligent processing to be deployed in aircraft for the detection and isolation of global and incipient failures.