Sensor-integrating, “gentelligent” components allow to “inherit” operational loads-data for design optimisations from one generation to the next. For area-wide acquisition and reliable transmission of this data, wireless sensor networks (WSN) are often used, but small sensor nodes for reconstructing deformations and loads, so-called shape sensing, are rarely considered as well as a methodical development of such sensor nodes. This paper presents the development of a small sensor node in accordance to the VDI 2206 for shape sensing with a prototype with strain gauges, HX711 A/D converters and an Arduino Nano 33 IoT microprocessor. An infrastructured WSN is built and tested on an aluminium part at a test rig. The shape sensing is carried out with three sensor nodes and the deformed shape is displayed on a server-website to demonstrate the functionality of the methodically developed WSN.

Information regarding deformations in large and complex systems is necessary in the prediction of structural failures caused by un-natural flexural occurrences. Sensing systems which are used to predict shapes, in order to develop a global surface picture require high precision and lower time lag. In this work, a unique bio-inspired training mechanism for support vector regression is presented for shape sensing in structures mounted with Fiber Bragg Gratings. Experimental validation was carried out on a simply supported beam, loaded at different positions and an aircraft wing model for different types of bending. The resulting deflections at specified locations along the length of the beam and on both surfaces of the wing were interpreted from the wavelength shifts of the corresponding Fiber Bragg Gratings through the specially modified Support Vector Regression. The method has shown high accuracy, low computational requirements and enhanced prediction times. The proposed bio-inspired training method has also been compared with two conventional training methodologies.