Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring
The identification and separation of sound sources has always been a difficult problem for acoustic technicians to tackle. This is due to the considerable complexity of a sound that is made up of many contributions at different frequencies. Each sound has a specific frequency spectrum, but when many...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/14/6488 |
| _version_ | 1797527635666927616 |
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| author | Giuseppe Ciaburro Gino Iannace Virginia Puyana-Romero Amelia Trematerra |
| author_facet | Giuseppe Ciaburro Gino Iannace Virginia Puyana-Romero Amelia Trematerra |
| author_sort | Giuseppe Ciaburro |
| collection | DOAJ |
| description | The identification and separation of sound sources has always been a difficult problem for acoustic technicians to tackle. This is due to the considerable complexity of a sound that is made up of many contributions at different frequencies. Each sound has a specific frequency spectrum, but when many sounds overlap it becomes difficult to discriminate between the different contributions. In this case, it can be extremely useful to have a tool that is capable of identifying the operating conditions of an acoustic source. In this study, measurements were made of the noise emitted by a wind turbine in the vicinity of a sensitive receptor. To identify the operating conditions of the wind turbine, average spectral levels in one-third octave bands were used. A model based on a support vector machine (SVM) was developed for the detection of the operating conditions of the wind turbine; then a model based on an artificial neural network was used to compare the performance of both models. The high precision returned by the simulation models supports the adoption of these tools as a support for the acoustic characterization of noise in environments close to wind turbines. |
| first_indexed | 2024-03-10T09:45:35Z |
| format | Article |
| id | doaj.art-02d4c544a6534244a090f16fe6c2f2e5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:45:35Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-02d4c544a6534244a090f16fe6c2f2e52023-11-22T03:10:32ZengMDPI AGApplied Sciences2076-34172021-07-011114648810.3390/app11146488Machine Learning-Based Tools for Wind Turbine Acoustic MonitoringGiuseppe Ciaburro0Gino Iannace1Virginia Puyana-Romero2Amelia Trematerra3Department of Architecture and Industrial Design, Università degli Studi della Campania Luigi Vanvitelli, Borgo San Lorenzo, 81031 Aversa, ItalyDepartment of Architecture and Industrial Design, Università degli Studi della Campania Luigi Vanvitelli, Borgo San Lorenzo, 81031 Aversa, ItalyDepartment of Sound and Acoustic Engineering, Universidad de Las Américas, Quito EC170125, EcuadorDepartment of Architecture and Industrial Design, Università degli Studi della Campania Luigi Vanvitelli, Borgo San Lorenzo, 81031 Aversa, ItalyThe identification and separation of sound sources has always been a difficult problem for acoustic technicians to tackle. This is due to the considerable complexity of a sound that is made up of many contributions at different frequencies. Each sound has a specific frequency spectrum, but when many sounds overlap it becomes difficult to discriminate between the different contributions. In this case, it can be extremely useful to have a tool that is capable of identifying the operating conditions of an acoustic source. In this study, measurements were made of the noise emitted by a wind turbine in the vicinity of a sensitive receptor. To identify the operating conditions of the wind turbine, average spectral levels in one-third octave bands were used. A model based on a support vector machine (SVM) was developed for the detection of the operating conditions of the wind turbine; then a model based on an artificial neural network was used to compare the performance of both models. The high precision returned by the simulation models supports the adoption of these tools as a support for the acoustic characterization of noise in environments close to wind turbines.https://www.mdpi.com/2076-3417/11/14/6488wind turbinenoise measurementslow-frequency soundartificial neural networkpattern recognitionsupport vector machine |
| spellingShingle | Giuseppe Ciaburro Gino Iannace Virginia Puyana-Romero Amelia Trematerra Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring Applied Sciences wind turbine noise measurements low-frequency sound artificial neural network pattern recognition support vector machine |
| title | Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring |
| title_full | Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring |
| title_fullStr | Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring |
| title_full_unstemmed | Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring |
| title_short | Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring |
| title_sort | machine learning based tools for wind turbine acoustic monitoring |
| topic | wind turbine noise measurements low-frequency sound artificial neural network pattern recognition support vector machine |
| url | https://www.mdpi.com/2076-3417/11/14/6488 |
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