Force Profile Characterization for Thermostatic Bimetal using Extreme Learning Machine



Bimetallic Strip, extre, Neural Network, Part Average Testing


The force-displacement profile is a key parameter in
manufacturing electric motor thermo-protectors, hence its accurate estimation helps preventing malfunctions due to overheating and/or short-circuits. In this research, we propose a novel force
profile characterizer based on a Machine Learning algorithm (ML), the Extreme Learning Machine (ELM). Here, we combine the ELM with a Partial Average Test filter (PAT) to predict the behavior of thermostatic bimetallic strips. The computational
efficiency inherent to ELMs allows the use of the algorithm (PATELM) in real manufacturing environments, where computational resources tend to be limited and response time is of the utmost
importance. The algorithm results were compared with actual measurements taken from production samples following ASTM B106-08, and the force-displacement profile of the thermostatic bimetallic strips measurements. The results show a correlation
in excess of 86% including batches smaller than 50 samples. This result was constant even in cases where measurements were affected by noise present in industrial environments. The time required to obtain the strength profile was significantly lower
than alternative methods, making this algorithm suitable for IoT systems.


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