Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery
| Autor: | Rostislav Huzlik, Tomas Marada, Karla Maradova, Daniel Zuth, Jiri Tuma, Vojtech Frkal, Petr Blecha |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
MATLAB
Control and Optimization business.product_category Computer science classification learner app computer.software_genre Industrial and Manufacturing Engineering Field (computer science) Machining TJ1-1570 Computer Science (miscellaneous) Mechanical engineering and machinery Time domain Electrical and Electronic Engineering computer.programming_language vibrodiagnostics classification model Mechanical Engineering Python (programming language) Machine tool machine learning Control and Systems Engineering Frequency domain Numerical control unbalance Data mining business computer Python |
| Zdroj: | Machines Volume 9 Issue 10 Machines, Vol 9, Iss 222, p 222 (2021) Machines. 2021, vol. 9, issue 10, p. 1-19. |
| ISSN: | 2075-1702 |
| DOI: | 10.3390/machines9100222 |
| Popis: | The current digitization of industrial processes is leading to the development of smart machines and smart applications in the field of engineering technologies. The basis is an advanced sensor system that monitors selected characteristic values of the machine. The obtained data need to be further analysed, correctly interpreted, and visualized by the machine operator. Thus the machine operator can gain a sixth sense for keeping the machine and the production process in a suitable condition. This has a positive effect on reducing the stress load on the operator in the production of expensive components and in monitoring the safe condition of the machine. The key element here is the use of a suitable classification model for data evaluation of the monitored machine parameters. The article deals with the comparison of the success rate of classification models from the MATLAB Classification Learner App. Classification models will compare data from the frequency and time domain, the data source is the same. Both data samples are from real measurements on the CNC vertical machining center (CNC-Computer Numerical Control). Three basic states representing machine tool damage are recognized. The data are then processed and reduced for the use of the MATLAB Classification Learner app, which creates a model for recognizing faults. The article aims to compare the success rate of classification models when the data source is a dataset in time or frequency domain and combination. |
| Databáze: | OpenAIRE |
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