Machine learning-based software sensors for machine state monitoring - The role of SMOTE-based data augmentation
A method for flexible vibration sensor-based retrofitting of CNC machines is proposed. As different states leave different fingerprints in the power spectrum plane, the states of the machine can be distinguished based on the features extracted from the spectrum map. Due to some states, like tool rep...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123022004480 |
| _version_ | 1811216393830400000 |
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| author | Alex Kummer Tamás Ruppert Tibor Medvegy János Abonyi |
| author_facet | Alex Kummer Tamás Ruppert Tibor Medvegy János Abonyi |
| author_sort | Alex Kummer |
| collection | DOAJ |
| description | A method for flexible vibration sensor-based retrofitting of CNC machines is proposed. As different states leave different fingerprints in the power spectrum plane, the states of the machine can be distinguished based on the features extracted from the spectrum map. Due to some states, like tool replacement, are less frequent than others, like production state, monitoring the machine states is considered an imbalanced classification problem. The key idea is to use Borderline-Synthetic Minority Oversampling Technique (Borderline-SMOTE) to augment the data set. The concept is validated in an industrial case study. Soft sensors based on four machine learning algorithms with and without SMOTE to predict the states of the machine were implemented. The results show that the SMOTE-based data augmentation improved the performance of the models by 50%. |
| first_indexed | 2024-04-12T06:38:22Z |
| format | Article |
| id | doaj.art-b77a0164ce324bd7b76127440fb45966 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-12T06:38:22Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-b77a0164ce324bd7b76127440fb459662022-12-22T03:43:48ZengElsevierResults in Engineering2590-12302022-12-0116100778Machine learning-based software sensors for machine state monitoring - The role of SMOTE-based data augmentationAlex Kummer0Tamás Ruppert1Tibor Medvegy2János Abonyi3ELKH-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, POB 158, Veszprém, H-8200, Hungary; Corresponding author.ELKH-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, POB 158, Veszprém, H-8200, HungaryMechatronics and Measurement Techniques Research Group, University of Pannonia, Egyetem u. 10, H-8200, Veszprém, HungaryELKH-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, POB 158, Veszprém, H-8200, HungaryA method for flexible vibration sensor-based retrofitting of CNC machines is proposed. As different states leave different fingerprints in the power spectrum plane, the states of the machine can be distinguished based on the features extracted from the spectrum map. Due to some states, like tool replacement, are less frequent than others, like production state, monitoring the machine states is considered an imbalanced classification problem. The key idea is to use Borderline-Synthetic Minority Oversampling Technique (Borderline-SMOTE) to augment the data set. The concept is validated in an industrial case study. Soft sensors based on four machine learning algorithms with and without SMOTE to predict the states of the machine were implemented. The results show that the SMOTE-based data augmentation improved the performance of the models by 50%.http://www.sciencedirect.com/science/article/pii/S2590123022004480Soft-sensorRetrofittingPower spectrumIndustry 4.0 |
| spellingShingle | Alex Kummer Tamás Ruppert Tibor Medvegy János Abonyi Machine learning-based software sensors for machine state monitoring - The role of SMOTE-based data augmentation Results in Engineering Soft-sensor Retrofitting Power spectrum Industry 4.0 |
| title | Machine learning-based software sensors for machine state monitoring - The role of SMOTE-based data augmentation |
| title_full | Machine learning-based software sensors for machine state monitoring - The role of SMOTE-based data augmentation |
| title_fullStr | Machine learning-based software sensors for machine state monitoring - The role of SMOTE-based data augmentation |
| title_full_unstemmed | Machine learning-based software sensors for machine state monitoring - The role of SMOTE-based data augmentation |
| title_short | Machine learning-based software sensors for machine state monitoring - The role of SMOTE-based data augmentation |
| title_sort | machine learning based software sensors for machine state monitoring the role of smote based data augmentation |
| topic | Soft-sensor Retrofitting Power spectrum Industry 4.0 |
| url | http://www.sciencedirect.com/science/article/pii/S2590123022004480 |
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