A machine sound monitoring for predictive maintenance focusing on very low frequency band
The monitoring of machines is one of key issues in the Industry 4.0 era. Particularly, the monitoring realized by non-contact sensors is drawing attention since it is easy to install and to avoid any problems caused by sensors accidentally dropping down into the machines. For example, sound monitori...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-01-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2020.1863611 |
| _version_ | 1797661145975226368 |
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| author | Kazuki Tsuji Shota Imai Ryota Takao Tomonori Kimura Hitoshi Kondo Yukihiro Kamiya |
| author_facet | Kazuki Tsuji Shota Imai Ryota Takao Tomonori Kimura Hitoshi Kondo Yukihiro Kamiya |
| author_sort | Kazuki Tsuji |
| collection | DOAJ |
| description | The monitoring of machines is one of key issues in the Industry 4.0 era. Particularly, the monitoring realized by non-contact sensors is drawing attention since it is easy to install and to avoid any problems caused by sensors accidentally dropping down into the machines. For example, sound monitoring satisfies this requirement. In this paper, we propose to apply the Accumulation for Real-time Serial-to-parallel Converter (ARS) for the monitoring of machine sounds to analyse low frequency bands which have not been sufficiently investigated so far. The machine sounds captured in a real factory are analysed so that the change of the machine sounds which varies in accordance with machine status is detected. It is verified that ARS successfully detects the difference as precise as wavelet transform (WT) with Morlet wavelet even though its computational load is significantly lower than that of WT. |
| first_indexed | 2024-03-11T18:40:12Z |
| format | Article |
| id | doaj.art-d44fbf90fc4f4ee68823a4a430055a01 |
| institution | Directory Open Access Journal |
| issn | 1884-9970 |
| language | English |
| last_indexed | 2024-03-11T18:40:12Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj.art-d44fbf90fc4f4ee68823a4a430055a012023-10-12T13:36:25ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702021-01-01141273810.1080/18824889.2020.18636111863611A machine sound monitoring for predictive maintenance focusing on very low frequency bandKazuki Tsuji0Shota Imai1Ryota Takao2Tomonori Kimura3Hitoshi Kondo4Yukihiro Kamiya5Department of Information Science and Technology, Aichi Prefectural UniversityDepartment of Information Science and Technology, Aichi Prefectural UniversityDepartment of Information Science and Technology, Aichi Prefectural UniversityCosmotec Co. Ltd.Cosmotec Co. Ltd.Department of Information Science and Technology, Aichi Prefectural UniversityThe monitoring of machines is one of key issues in the Industry 4.0 era. Particularly, the monitoring realized by non-contact sensors is drawing attention since it is easy to install and to avoid any problems caused by sensors accidentally dropping down into the machines. For example, sound monitoring satisfies this requirement. In this paper, we propose to apply the Accumulation for Real-time Serial-to-parallel Converter (ARS) for the monitoring of machine sounds to analyse low frequency bands which have not been sufficiently investigated so far. The machine sounds captured in a real factory are analysed so that the change of the machine sounds which varies in accordance with machine status is detected. It is verified that ARS successfully detects the difference as precise as wavelet transform (WT) with Morlet wavelet even though its computational load is significantly lower than that of WT.http://dx.doi.org/10.1080/18824889.2020.1863611sound monitoringmachine soundarsfftlow frequency |
| spellingShingle | Kazuki Tsuji Shota Imai Ryota Takao Tomonori Kimura Hitoshi Kondo Yukihiro Kamiya A machine sound monitoring for predictive maintenance focusing on very low frequency band SICE Journal of Control, Measurement, and System Integration sound monitoring machine sound ars fft low frequency |
| title | A machine sound monitoring for predictive maintenance focusing on very low frequency band |
| title_full | A machine sound monitoring for predictive maintenance focusing on very low frequency band |
| title_fullStr | A machine sound monitoring for predictive maintenance focusing on very low frequency band |
| title_full_unstemmed | A machine sound monitoring for predictive maintenance focusing on very low frequency band |
| title_short | A machine sound monitoring for predictive maintenance focusing on very low frequency band |
| title_sort | machine sound monitoring for predictive maintenance focusing on very low frequency band |
| topic | sound monitoring machine sound ars fft low frequency |
| url | http://dx.doi.org/10.1080/18824889.2020.1863611 |
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