Improved Entropy-Based Condition Monitoring for Pressure Pipeline Through Acoustic Denoising
During long-term operation in complex environments, the pressure pipeline systems are prone to damage and faults, and serious safety accidents may occur without real-time condition monitoring. Moreover, in traditional non-contact monitoring approaches, acoustic signals are widely employed for condit...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/1/10 |
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| _version_ | 1832588544918421504 |
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| author | Yu Wan Shaochen Lin Chuanling Jin Yan Gao Yang Yang |
| author_facet | Yu Wan Shaochen Lin Chuanling Jin Yan Gao Yang Yang |
| author_sort | Yu Wan |
| collection | DOAJ |
| description | During long-term operation in complex environments, the pressure pipeline systems are prone to damage and faults, and serious safety accidents may occur without real-time condition monitoring. Moreover, in traditional non-contact monitoring approaches, acoustic signals are widely employed for condition monitoring for pressure pipelines, which are easily contaminated by background noise and provide unsatisfactory accuracy. As a tool for quantifying uncertainty and complexity, signal entropy is applied to detect abnormal conditions. Based on the characteristics of entropy and acoustic signals, an improved entropy-based condition monitoring method is proposed for pressure pipelines through acoustic denoising. Specifically, this improved entropy-based noise reduction model is proposed to reduce the noise of monitoring acoustic signals through adversarial training. Based on the denoising of acoustic signals, an abnormal sound detection method is proposed to realize condition monitoring for pressure pipelines. In addition, the experimental platform is built to test the effectiveness and reliability of the proposed method. The results indicate that the quality of signal denoising can reach over 3 dB, while the accuracy of condition monitoring is about 92% for different conditions. Finally, the superiority of the proposed method is verified by comparing it with other methods. |
| format | Article |
| id | doaj-art-40e7204c095043379493b3bc3bc476f2 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-40e7204c095043379493b3bc3bc476f22025-01-24T13:31:38ZengMDPI AGEntropy1099-43002024-12-012711010.3390/e27010010Improved Entropy-Based Condition Monitoring for Pressure Pipeline Through Acoustic DenoisingYu Wan0Shaochen Lin1Chuanling Jin2Yan Gao3Yang Yang4Jiangsu Frontier Electric Technology Co., Ltd., Nanjing 211102, ChinaJiangsu Frontier Electric Technology Co., Ltd., Nanjing 211102, ChinaJiangsu Frontier Electric Technology Co., Ltd., Nanjing 211102, ChinaJiangsu Frontier Electric Technology Co., Ltd., Nanjing 211102, ChinaJiangsu Frontier Electric Technology Co., Ltd., Nanjing 211102, ChinaDuring long-term operation in complex environments, the pressure pipeline systems are prone to damage and faults, and serious safety accidents may occur without real-time condition monitoring. Moreover, in traditional non-contact monitoring approaches, acoustic signals are widely employed for condition monitoring for pressure pipelines, which are easily contaminated by background noise and provide unsatisfactory accuracy. As a tool for quantifying uncertainty and complexity, signal entropy is applied to detect abnormal conditions. Based on the characteristics of entropy and acoustic signals, an improved entropy-based condition monitoring method is proposed for pressure pipelines through acoustic denoising. Specifically, this improved entropy-based noise reduction model is proposed to reduce the noise of monitoring acoustic signals through adversarial training. Based on the denoising of acoustic signals, an abnormal sound detection method is proposed to realize condition monitoring for pressure pipelines. In addition, the experimental platform is built to test the effectiveness and reliability of the proposed method. The results indicate that the quality of signal denoising can reach over 3 dB, while the accuracy of condition monitoring is about 92% for different conditions. Finally, the superiority of the proposed method is verified by comparing it with other methods.https://www.mdpi.com/1099-4300/27/1/10condition monitoringsignal entropypressure pipelineacoustic denoisingabnormal sound detection |
| spellingShingle | Yu Wan Shaochen Lin Chuanling Jin Yan Gao Yang Yang Improved Entropy-Based Condition Monitoring for Pressure Pipeline Through Acoustic Denoising Entropy condition monitoring signal entropy pressure pipeline acoustic denoising abnormal sound detection |
| title | Improved Entropy-Based Condition Monitoring for Pressure Pipeline Through Acoustic Denoising |
| title_full | Improved Entropy-Based Condition Monitoring for Pressure Pipeline Through Acoustic Denoising |
| title_fullStr | Improved Entropy-Based Condition Monitoring for Pressure Pipeline Through Acoustic Denoising |
| title_full_unstemmed | Improved Entropy-Based Condition Monitoring for Pressure Pipeline Through Acoustic Denoising |
| title_short | Improved Entropy-Based Condition Monitoring for Pressure Pipeline Through Acoustic Denoising |
| title_sort | improved entropy based condition monitoring for pressure pipeline through acoustic denoising |
| topic | condition monitoring signal entropy pressure pipeline acoustic denoising abnormal sound detection |
| url | https://www.mdpi.com/1099-4300/27/1/10 |
| work_keys_str_mv | AT yuwan improvedentropybasedconditionmonitoringforpressurepipelinethroughacousticdenoising AT shaochenlin improvedentropybasedconditionmonitoringforpressurepipelinethroughacousticdenoising AT chuanlingjin improvedentropybasedconditionmonitoringforpressurepipelinethroughacousticdenoising AT yangao improvedentropybasedconditionmonitoringforpressurepipelinethroughacousticdenoising AT yangyang improvedentropybasedconditionmonitoringforpressurepipelinethroughacousticdenoising |