Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis
The effect of noise on the human body has attracted increasing research attention. In particular, many factories generate motor noise pollution, which exposes general workers to noise for extended periods. To solve this problem, masks made of different materials are used for reducing the noise gener...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9830711/ |
| _version_ | 1818042144305184768 |
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| author | Po-Jiun Wen Chihpin Huang |
| author_facet | Po-Jiun Wen Chihpin Huang |
| author_sort | Po-Jiun Wen |
| collection | DOAJ |
| description | The effect of noise on the human body has attracted increasing research attention. In particular, many factories generate motor noise pollution, which exposes general workers to noise for extended periods. To solve this problem, masks made of different materials are used for reducing the noise generated by motors. In this study, we attempted to predict the acoustic sound of masked motors. We collected noise level data in decibels for different operation frequencies of motors used at National Synchrotron Radiation Research Center (NSRRC) and developed a machine learning model according to the characteristics of the collected data to simulate the effect of masks on the motor sound. We use the Gradient Boost Model (GBM) as the main learning method because the model is suitable for predicting noise from comparison results of the five models are very common predictive models and may performed as compare method to predict acoustic noise. The results indicated that the prediction accuracy of the GBM was considerably higher than other four traditional machine learning methods (random forests, support vector machine, gaussian processes regression model and multiple linear regression models). Moreover, we used a general multiple linear regression method as the worst method of comparison and conducted time–frequency visualization of the sound for analysis. At NSRRC, we examined the effects of three observation locations and three mask materials, namely wood, metal, and acrylic, on the sound prediction accuracy achieved with the developed model. The highest sound prediction accuracy was obtained behind the motor and under an acrylic mask. |
| first_indexed | 2024-12-10T08:41:39Z |
| format | Article |
| id | doaj.art-66e88bf953c744ef929e7811a2d6ccc2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T08:41:39Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-66e88bf953c744ef929e7811a2d6ccc22022-12-22T01:55:51ZengIEEEIEEE Access2169-35362022-01-0110757087571910.1109/ACCESS.2022.31914339830711Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise AnalysisPo-Jiun Wen0https://orcid.org/0000-0003-2417-0170Chihpin Huang1Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanInstitute of Environmental Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanThe effect of noise on the human body has attracted increasing research attention. In particular, many factories generate motor noise pollution, which exposes general workers to noise for extended periods. To solve this problem, masks made of different materials are used for reducing the noise generated by motors. In this study, we attempted to predict the acoustic sound of masked motors. We collected noise level data in decibels for different operation frequencies of motors used at National Synchrotron Radiation Research Center (NSRRC) and developed a machine learning model according to the characteristics of the collected data to simulate the effect of masks on the motor sound. We use the Gradient Boost Model (GBM) as the main learning method because the model is suitable for predicting noise from comparison results of the five models are very common predictive models and may performed as compare method to predict acoustic noise. The results indicated that the prediction accuracy of the GBM was considerably higher than other four traditional machine learning methods (random forests, support vector machine, gaussian processes regression model and multiple linear regression models). Moreover, we used a general multiple linear regression method as the worst method of comparison and conducted time–frequency visualization of the sound for analysis. At NSRRC, we examined the effects of three observation locations and three mask materials, namely wood, metal, and acrylic, on the sound prediction accuracy achieved with the developed model. The highest sound prediction accuracy was obtained behind the motor and under an acrylic mask.https://ieeexplore.ieee.org/document/9830711/Gradient boosting model (GBM)machine learningmotor noise predictiontime-frequency diagrams |
| spellingShingle | Po-Jiun Wen Chihpin Huang Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis IEEE Access Gradient boosting model (GBM) machine learning motor noise prediction time-frequency diagrams |
| title | Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis |
| title_full | Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis |
| title_fullStr | Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis |
| title_full_unstemmed | Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis |
| title_short | Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis |
| title_sort | machine learning and prediction of masked motors with different materials based on noise analysis |
| topic | Gradient boosting model (GBM) machine learning motor noise prediction time-frequency diagrams |
| url | https://ieeexplore.ieee.org/document/9830711/ |
| work_keys_str_mv | AT pojiunwen machinelearningandpredictionofmaskedmotorswithdifferentmaterialsbasedonnoiseanalysis AT chihpinhuang machinelearningandpredictionofmaskedmotorswithdifferentmaterialsbasedonnoiseanalysis |