Influence of Environmental Noise on Quality Control of HVAC Devices Based on Convolutional Neural Network
Source abstract
Abstract
Testing the quality of manufactured products based on their sound expression is becoming popular nowadays. To maintain low production costs, the testing is processed at the end of the assembly line. Such measurements are affected considerably by the factory noise even though they are performed in anechoic chambers. Before designing the quality control algorithm based on a convolutional neural network, we do not know the influence of the factory noise on the success rate of the algorithm that can potentially be obtained. Therefore, this contribution addresses this problem. The experiments were undertaken on a synthetic dataset of heat, ventilation, and air-conditioning devices. The results show that classification accuracy of the decision-making algorithm declines more rapidly at a high level of environmental noise.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Neural Networks and Applications · Advanced machining processes and optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Control Systems and Instrumentation, VŠB—Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava, Czech Republic
ELCOM, a.s., Technologická 374/6, 708 00 Ostrava, Czech Republic
Department of Measurements and Control Systems, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland
Funding
European Regional Development Fund
References (62)
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