Multiscale Entropy-Based Feature Extraction for the Detection of Instability Inception in Axial Compressors
The detection of instability inception is favorable to avoid compressor instability. In this paper, a multiscale entropy-based feature extraction is developed for the detection of the instability inception in axial compressors. Nonlinear and statistical features of the short-time instability incepti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/26/1/48 |
| _version_ | 1797344059058028544 |
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| author | Yihan Fu Zheng Zhao Peng Lin |
| author_facet | Yihan Fu Zheng Zhao Peng Lin |
| author_sort | Yihan Fu |
| collection | DOAJ |
| description | The detection of instability inception is favorable to avoid compressor instability. In this paper, a multiscale entropy-based feature extraction is developed for the detection of the instability inception in axial compressors. Nonlinear and statistical features of the short-time instability inception are extracted by generally combining multiscale entropy and statistical features. First, nonlinear features are extracted by refined composite multiscale entropy to avoid the inaccurate estimation or undefined entropy of multiscale entropy for short time series. Second, the time-domain-based statistical features are chosen to capture more information on instability inception, and the dominant statistical features are determined by random forests implemented with the mean decrease accuracy algorithm at each time scale. The obtained refined composite dominant statistical features are regarded as weighting factors and integrated with the refined composite multiscale entropy to generate a combined feature. Finally, numerical simulation results on two synthetic noise datasets and a compressor instability model dataset are presented to demonstrate the effectiveness, efficiency, and robustness of the combined features under different conditions. |
| first_indexed | 2024-03-08T10:56:51Z |
| format | Article |
| id | doaj.art-07b8bac441234b72a3e486969e91ceb8 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-08T10:56:51Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-07b8bac441234b72a3e486969e91ceb82024-01-26T16:23:07ZengMDPI AGEntropy1099-43002024-01-012614810.3390/e26010048Multiscale Entropy-Based Feature Extraction for the Detection of Instability Inception in Axial CompressorsYihan Fu0Zheng Zhao1Peng Lin2School of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaThe detection of instability inception is favorable to avoid compressor instability. In this paper, a multiscale entropy-based feature extraction is developed for the detection of the instability inception in axial compressors. Nonlinear and statistical features of the short-time instability inception are extracted by generally combining multiscale entropy and statistical features. First, nonlinear features are extracted by refined composite multiscale entropy to avoid the inaccurate estimation or undefined entropy of multiscale entropy for short time series. Second, the time-domain-based statistical features are chosen to capture more information on instability inception, and the dominant statistical features are determined by random forests implemented with the mean decrease accuracy algorithm at each time scale. The obtained refined composite dominant statistical features are regarded as weighting factors and integrated with the refined composite multiscale entropy to generate a combined feature. Finally, numerical simulation results on two synthetic noise datasets and a compressor instability model dataset are presented to demonstrate the effectiveness, efficiency, and robustness of the combined features under different conditions.https://www.mdpi.com/1099-4300/26/1/48multiscale entropynonlinear featureshort time seriesinstability inceptionaxial compressors |
| spellingShingle | Yihan Fu Zheng Zhao Peng Lin Multiscale Entropy-Based Feature Extraction for the Detection of Instability Inception in Axial Compressors Entropy multiscale entropy nonlinear feature short time series instability inception axial compressors |
| title | Multiscale Entropy-Based Feature Extraction for the Detection of Instability Inception in Axial Compressors |
| title_full | Multiscale Entropy-Based Feature Extraction for the Detection of Instability Inception in Axial Compressors |
| title_fullStr | Multiscale Entropy-Based Feature Extraction for the Detection of Instability Inception in Axial Compressors |
| title_full_unstemmed | Multiscale Entropy-Based Feature Extraction for the Detection of Instability Inception in Axial Compressors |
| title_short | Multiscale Entropy-Based Feature Extraction for the Detection of Instability Inception in Axial Compressors |
| title_sort | multiscale entropy based feature extraction for the detection of instability inception in axial compressors |
| topic | multiscale entropy nonlinear feature short time series instability inception axial compressors |
| url | https://www.mdpi.com/1099-4300/26/1/48 |
| work_keys_str_mv | AT yihanfu multiscaleentropybasedfeatureextractionforthedetectionofinstabilityinceptioninaxialcompressors AT zhengzhao multiscaleentropybasedfeatureextractionforthedetectionofinstabilityinceptioninaxialcompressors AT penglin multiscaleentropybasedfeatureextractionforthedetectionofinstabilityinceptioninaxialcompressors |