The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear Monitoring
Friction between metals is a physical phenomenon that occurs in manufacturing machine tools. This annoying noise implies unnecessary metal contact and deterioration of a mechanical system. In this study, for the monitoring of the friction between two metal surfaces, the acoustic signature was extrac...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/9/3755 |
| _version_ | 1797536888890851328 |
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| author | Yeonuk Seong Donghyeon Lee Jihye Yeom Junhong Park |
| author_facet | Yeonuk Seong Donghyeon Lee Jihye Yeom Junhong Park |
| author_sort | Yeonuk Seong |
| collection | DOAJ |
| description | Friction between metals is a physical phenomenon that occurs in manufacturing machine tools. This annoying noise implies unnecessary metal contact and deterioration of a mechanical system. In this study, for the monitoring of the friction between two metal surfaces, the acoustic signature was extracted by applying the wavelet transform method to the noise measured from the change in contact force for each state of adhesive and abrasive wear. Experiments were conducted with a constant relative speed between the contacting metal surfaces. For the adhesive wear, the peak signal-to-noise ratio (PSNR) calculated by the wavelet transformation increases with the increasing contact pressure. Opposite trends were observed for the abrasive wear. The proposed index formed a group within a specific range. This ratio exhibited a strong relationship with the wear characteristics and the surface condition. From the proposed index calculated by the wavelet coefficients, the continuous monitoring of the wear influence on the failure of the machine movement operations is achieved by the sound radiation from the contacting surfaces. |
| first_indexed | 2024-03-10T12:07:15Z |
| format | Article |
| id | doaj.art-fb8b3e01a30d48a194b065d4bbcf7dd8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:07:15Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fb8b3e01a30d48a194b065d4bbcf7dd82023-11-21T16:32:39ZengMDPI AGApplied Sciences2076-34172021-04-01119375510.3390/app11093755The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear MonitoringYeonuk Seong0Donghyeon Lee1Jihye Yeom2Junhong Park3Department of Mechanical Engineering, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, KoreaDepartment of Mechanical Engineering, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, KoreaDepartment of Mechanical Engineering, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, KoreaDepartment of Mechanical Engineering, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, KoreaFriction between metals is a physical phenomenon that occurs in manufacturing machine tools. This annoying noise implies unnecessary metal contact and deterioration of a mechanical system. In this study, for the monitoring of the friction between two metal surfaces, the acoustic signature was extracted by applying the wavelet transform method to the noise measured from the change in contact force for each state of adhesive and abrasive wear. Experiments were conducted with a constant relative speed between the contacting metal surfaces. For the adhesive wear, the peak signal-to-noise ratio (PSNR) calculated by the wavelet transformation increases with the increasing contact pressure. Opposite trends were observed for the abrasive wear. The proposed index formed a group within a specific range. This ratio exhibited a strong relationship with the wear characteristics and the surface condition. From the proposed index calculated by the wavelet coefficients, the continuous monitoring of the wear influence on the failure of the machine movement operations is achieved by the sound radiation from the contacting surfaces.https://www.mdpi.com/2076-3417/11/9/3755frictionacoustic noiseadhesive wearabrasive wearwavelet coefficient |
| spellingShingle | Yeonuk Seong Donghyeon Lee Jihye Yeom Junhong Park The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear Monitoring Applied Sciences friction acoustic noise adhesive wear abrasive wear wavelet coefficient |
| title | The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear Monitoring |
| title_full | The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear Monitoring |
| title_fullStr | The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear Monitoring |
| title_full_unstemmed | The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear Monitoring |
| title_short | The Feature Extraction through Wavelet Coefficients of Metal Friction Noise for Adhesive and Abrasive Wear Monitoring |
| title_sort | feature extraction through wavelet coefficients of metal friction noise for adhesive and abrasive wear monitoring |
| topic | friction acoustic noise adhesive wear abrasive wear wavelet coefficient |
| url | https://www.mdpi.com/2076-3417/11/9/3755 |
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