{"id":1787,"date":"2026-08-28T11:12:02","date_gmt":"2026-08-28T09:12:02","guid":{"rendered":"https:\/\/www.hsu-hh.de\/emt\/?page_id=1787"},"modified":"2026-08-28T13:59:07","modified_gmt":"2026-08-28T11:59:07","slug":"real-time-wireless-sensor-network-for-measuring-shape-and-strain-in-large-structures","status":"publish","type":"page","link":"https:\/\/www.hsu-hh.de\/emt\/en\/research-topics\/real-time-wireless-sensor-network-for-measuring-shape-and-strain-in-large-structures","title":{"rendered":"Real-time wireless sensor network for measuring shape and strain in large structures"},"content":{"rendered":"

Real-time measurement of large-scale, deformable components has so far been possible only to a limited extent in industrial manufacturing environments. At the same time, precise information about shape, deformation, and local strain is of great importance, for example, in form-forming manufacturing processes, the assembly of large subassemblies, or process monitoring in the aerospace and wind energy industries. The sensor network developed addresses this challenge with a modular, wireless, and scalable measurement system. A large number of distributed measurement nodes simultaneously capture data from the component\u2019s surface, ensuring that the measurement time remains independent of the object\u2019s size and allowing the network to be flexibly adapted to different geometries and measurement tasks. The sensor network developed at the Chair of Electrical Measurement Technology consists of measurement nodes for recording the local surface slope and connecting measurement rods for determining the relative distances between adjacent nodes. By combining these measured variables, the spatial positions of the measurement nodes as well as the shape and deformation of the component\u2019s surface are reconstructed. Because the measurement points are firmly assigned to the surface, local strains can also be determined. The measurement data is transmitted wirelessly to a central processing unit, where it is analyzed. The measurement nodes are vacuum-suctioned to the surface of the component for reusable deployment in the production environment.<\/p>\n\n\n\n

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Fig. 1: Connection between two measurement nodes, showing sensor specifications and mechanical dimensions.<\/figcaption><\/figure>\n<\/div>\n\n\n\n
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Fig. 2: Sensor network with seven measurement nodes and 12 connections: 1. Measurement node 2. Measurement rods 3. Vacuum hose.<\/figcaption><\/figure>\n<\/div><\/div>\n<\/div>\n\n\n

To validate the measurement system, repeatability and measurement accuracy are evaluated using two established reference measurement systems: photogrammetry and a laser tracker. For this purpose, an aircraft component (2.7 x 1.6 m) is deliberately deformed asymmetrically using two linear motors. The resulting deformation is recorded in parallel over 58 load cycles using the sensor network and both reference systems, and the results are then compared in terms of deviation and reproducibility.<\/p>\n\n\n

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Fig. 3: Comparative measurement between a sensor network and a conventional measurement system for large structures on a 1.6 x 2.7 m component<\/figcaption><\/figure>\n<\/div>\n\n

From the series of measurements, the deviation from the reference measurement systems and its standard deviation can be determined for each measurement node. No pronounced systematic offset is apparent. The maximum standard deviation is 0.34 mm, which is on a comparable order of magnitude to the measurement uncertainties of the reference systems used. In addition, the sensor network enables the determination of strain based on the positional changes of the measurement points fixed to the surface. This provides another relevant measurement parameter for process monitoring in addition to the change in shape. Local strains can also be recorded and show good agreement with the reference measurement systems used.<\/p>\n\n\n\n

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Fig. 4: Deviation of the sensor nodes from the reference systems.<\/figcaption><\/figure>\n<\/div>\n\n\n\n
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Fig. 5: Calculates strain based on data from the sensor network when the component is subjected to a one-sided load.<\/figcaption><\/figure>\n<\/div>\n<\/div>\n\n\n

The development and testing of the sensor network have been completed. The next step will focus on transferring the technology to concrete applications. To this end, we are seeking partners and users from industry and academia who would like to use the measurement system for new measurement tasks, process monitoring, or research. We look forward to discussing potential applications, collaborations, and application-specific adaptations!<\/strong>

Contact: lars.bretthauer@hsu-hh.de<\/strong><\/p>\n\n\n

Specifications:<\/strong><\/p>\n\n\n\n

\n

Measurement range: Measurement resolution
: Measurement accuracy
: Network structure
: Measurement frequency
: Data transmission
: Transmission error rate
: Maximum
operating time:<\/p>\n<\/div>\n\n\n\n

Max. 15 \u00d7 15 \u00d7 15 m (up to 120 measurement probes)
0.3 \u2013 0.6
0.15 \u2013 0.5 mm (depending on the network structure)
freely adjustable to the measurement task
5 \u2013 30 s (depending on environmental conditions)
IO-Link Wireless (cycle time: 5 ms)
< 10\u207b\u2079 (bit error rate)
up to 12 h<\/p>\n<\/div>\n<\/div>\n\n\n

Publications:<\/strong><\/p>\n\n\n\n

\"Publikation<\/div>
Lars-Michel Bretthauer, Ralf Heynicke, and Gerd Scholl. \u201cProposal of a Cyber-Physical Finite Element Sensor Network for Surface Measurements.\u201d In: 2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). May 2024, pp. 1\u20136. doi: 10.1109\/I2MTC60896.2024.10560668<\/a>.<\/div><\/div>\n\n\n\n
\"Publikation<\/div>
L.-M. Bretthauer, R. Heynicke, and G. Scholl. \u201cUncertainty Modeling of a Cyber-Physical Finite Element Sensor Network for Surface Measurements.\u201d In: 22nd GMA\/ITG Conference on Sensors and Measurement Systems 2024 (June 2024), pp. 380\u2013387. doi: 10.5162\/sensoren2024\/D5.1<\/a>.<\/div><\/div>\n\n\n\n
\"Publikation<\/div>
Lars-Michel Bretthauer, Ralf Heynicke, and Gerd Scholl. \u201cMonte Carlo Method for Uncertainty Estimations of Cyber-Physical Finite Element Sensor Networks.\u201d de. In: tm – Technisches Messen 91.s1 (Sept. 2024), pp. 32\u201337. ISSN: 2196-7113. DOI: 10.1515\/teme-2024-0050<\/a>.<\/div><\/div>\n\n\n\n
\"Publikation<\/div>
L. M. Bretthauer, G. Scholl, and R. Heynicke. \u201cShape Measurement of Large-Scale Components Using Wireless Sensor Networks.\u201d In: Nuremberg: AMA Service GmbH<\/abbr>, 2025, pp. 261\u2013262. doi: 10.5162\/smsi2025\/p15<\/a>.<\/div><\/div>\n\n\n\n
\"Publikation<\/div>
Lars-Michel Bretthauer, Ralf Heynicke, and Gerd Scholl. \u201cDesign Methodology and Uncertainty Estimation of a Wireless Sensor Network for Surface Strain and Shape Measurements.\u201d en. In: tm – Technisches Messen (Apr. 2025). ISSN: 0171-8096, 2196-7113. DOI: 10.1515\/teme-2025-0008<\/a>.<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"

Real-time measurement of large-scale, deformable components has so far been possible only to a limited extent in industrial manufacturing environments. At the same time, precise information about shape, deformation, and […]<\/p>\n","protected":false},"author":3352,"featured_media":0,"parent":1802,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1787","page","type-page","status-publish","hentry","category-research"],"lang":"en","translations":{"en":1787,"de":1667},"pll_sync_post":{},"_links":{"self":[{"href":"https:\/\/www.hsu-hh.de\/emt\/wp-json\/wp\/v2\/pages\/1787","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.hsu-hh.de\/emt\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.hsu-hh.de\/emt\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/emt\/wp-json\/wp\/v2\/users\/3352"}],"replies":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/emt\/wp-json\/wp\/v2\/comments?post=1787"}],"version-history":[{"count":2,"href":"https:\/\/www.hsu-hh.de\/emt\/wp-json\/wp\/v2\/pages\/1787\/revisions"}],"predecessor-version":[{"id":1906,"href":"https:\/\/www.hsu-hh.de\/emt\/wp-json\/wp\/v2\/pages\/1787\/revisions\/1906"}],"up":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/emt\/wp-json\/wp\/v2\/pages\/1802"}],"wp:attachment":[{"href":"https:\/\/www.hsu-hh.de\/emt\/wp-json\/wp\/v2\/media?parent=1787"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/emt\/wp-json\/wp\/v2\/categories?post=1787"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/emt\/wp-json\/wp\/v2\/tags?post=1787"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}