A Preliminary Fault Detection Methodology for Abnormal Distillation Column Operations Using Acoustic Signals
The fault detection of the chemical equipment operation process is an effective means to ensure safe production. In this study, an acoustic signal processing technique and a k-nearest neighbor (k-NN) classification algorithm were combined to identify the running states of the distillation columns. T...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/24/12657 |
| _version_ | 1797461653217869824 |
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| author | Guang-Yan Wang Zhen-Hao Yang Yan Zhang Hong-Hai Wang Zhi-Xi Zhang Bing-Jun Gao |
| author_facet | Guang-Yan Wang Zhen-Hao Yang Yan Zhang Hong-Hai Wang Zhi-Xi Zhang Bing-Jun Gao |
| author_sort | Guang-Yan Wang |
| collection | DOAJ |
| description | The fault detection of the chemical equipment operation process is an effective means to ensure safe production. In this study, an acoustic signal processing technique and a k-nearest neighbor (k-NN) classification algorithm were combined to identify the running states of the distillation columns. This method can accurately identify various fluid flow states in distillation columns, including normal and flooding states. First, the acoustic signals were collected under normal and abnormal states in an experimental distillation column. Then, the method of dual-domain feature extraction was used to extract the features such as the energy ratio and linear prediction coefficient (LPC). Moreover, the extracted feature parameters were analyzed and compared in a general way. Finally, the k-NN model was used to classify the acoustic signals. The results show that this method had high identification accuracy and provided an important reference for further research. |
| first_indexed | 2024-03-09T17:23:27Z |
| format | Article |
| id | doaj.art-d208377f68ff48beb91b29a6b1fbadae |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:23:27Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj.art-d208377f68ff48beb91b29a6b1fbadae2023-11-24T13:02:27ZengMDPI AGApplied Sciences2076-34172022-12-0112241265710.3390/app122412657A Preliminary Fault Detection Methodology for Abnormal Distillation Column Operations Using Acoustic SignalsGuang-Yan Wang0Zhen-Hao Yang1Yan Zhang2Hong-Hai Wang3Zhi-Xi Zhang4Bing-Jun Gao5School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Information Engineering, Tianjin University of Commerce, Tianjin 300134, ChinaSchool of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300130, ChinaNational-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, ChinaNational-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, ChinaNational-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, ChinaThe fault detection of the chemical equipment operation process is an effective means to ensure safe production. In this study, an acoustic signal processing technique and a k-nearest neighbor (k-NN) classification algorithm were combined to identify the running states of the distillation columns. This method can accurately identify various fluid flow states in distillation columns, including normal and flooding states. First, the acoustic signals were collected under normal and abnormal states in an experimental distillation column. Then, the method of dual-domain feature extraction was used to extract the features such as the energy ratio and linear prediction coefficient (LPC). Moreover, the extracted feature parameters were analyzed and compared in a general way. Finally, the k-NN model was used to classify the acoustic signals. The results show that this method had high identification accuracy and provided an important reference for further research.https://www.mdpi.com/2076-3417/12/24/12657distillation columnlinear prediction coefficientk-nearest neighboracoustic signalfault detection |
| spellingShingle | Guang-Yan Wang Zhen-Hao Yang Yan Zhang Hong-Hai Wang Zhi-Xi Zhang Bing-Jun Gao A Preliminary Fault Detection Methodology for Abnormal Distillation Column Operations Using Acoustic Signals Applied Sciences distillation column linear prediction coefficient k-nearest neighbor acoustic signal fault detection |
| title | A Preliminary Fault Detection Methodology for Abnormal Distillation Column Operations Using Acoustic Signals |
| title_full | A Preliminary Fault Detection Methodology for Abnormal Distillation Column Operations Using Acoustic Signals |
| title_fullStr | A Preliminary Fault Detection Methodology for Abnormal Distillation Column Operations Using Acoustic Signals |
| title_full_unstemmed | A Preliminary Fault Detection Methodology for Abnormal Distillation Column Operations Using Acoustic Signals |
| title_short | A Preliminary Fault Detection Methodology for Abnormal Distillation Column Operations Using Acoustic Signals |
| title_sort | preliminary fault detection methodology for abnormal distillation column operations using acoustic signals |
| topic | distillation column linear prediction coefficient k-nearest neighbor acoustic signal fault detection |
| url | https://www.mdpi.com/2076-3417/12/24/12657 |
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