Polynomial Identification of Strain Gauge Thermal Output...
AbstractStrain gauges are used in different areas, especially in thedesign and development of new technical constructions andmode/testing. Also, strain gauges are inc01porated in the functionalpart of many instruments and devices. They are mostlyused as sensors in transducers designed to measure such mechanicalquantities as forces, moments, pressures, accelerations,etc. They have an important role in shipbuilding and marinetransport in general. In this paper we have suggested andshown an approach to the identification of strain gauge thermaloutput curve on the example of a product available at themarket. Neural network simulated in MatLab has been applied.The neural network has been adapted to simulate a realsystem with 1 o-9 order of magnitude error_ As it is well known,strain gauges measure deformations of 1 o-o order of magnitude.It is obvious that the network eJTOr cannot influence the measurementresults because of its being smaller by three orders ofmagnitude.
(1] *, Strain Gauge Measurement -A Tutorial, National
Instruments, Application Note 078, 1998.
(2] K. Schulze, Experimentelle MejJtechnik in Maschinen
und Stahlbau, VE Verlang Technik, Berlin, 1988.
(3] I. Alfirevic, Nauka o cvrstoCi, Tehnicka knjiga, Zagreb,
(4] J. W. Dally, Experimental stress analysis, McGrow-Hill,
*,Strain Gauge Transducers,
(6] *,PCB Technical Support- Introduction to Piezoelectric
Pressure Sensors, http://www.pcb.com/tech_press.html
*,PCB Technical Support- Introduction to Piezoelectric
Force Sensors, http://www.pcb.com/tech_press.html
*, Strain Gauge Thermal Output and Gauge Factor
Variation with Temperature,
A. P. Roskilly, E. Mesbahi, Marine System Modeling
Using Artificial Neural Networks: an Introduction to the
The01y and Practice, TranslmarE, vol. 108, part 3, pp
(10] M. Norgaard, Neural Network Based Control System
Design Toolkit, Technical report 00-E-892, Technical
University of Denmark, 2000.
(11] L. Ljung, System Identification - Theory for the User,
(12] M. Norgaard, Neural Network Based System Identification
Toolbox, Technical report 00-E-891, Technical University
of Denmark, 2000.
(13] MathWorks, http://www.mathworks.com
(14] I. Vujovic, S. Beros, I. Kuzma nic, Neural Networks and
Wavelets in Ships and Autopilots Modeling and Identification,
SORT A 2000, Rijeka, Croatia, pp 149-156.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).