Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection
Online detection of fatigued wear debris in the lubricants of aero-engines can provide warning of engine failure during flight, thus having great economic and social benefits. In this paper, we propose a capacitance array sensor and a hyper-heuristic partial differential equation (PDE) inversion met...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/3/515 |
| _version_ | 1798038047265128448 |
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| author | Yanshan Sun Lecheng Jia Zhoumo Zeng |
| author_facet | Yanshan Sun Lecheng Jia Zhoumo Zeng |
| author_sort | Yanshan Sun |
| collection | DOAJ |
| description | Online detection of fatigued wear debris in the lubricants of aero-engines can provide warning of engine failure during flight, thus having great economic and social benefits. In this paper, we propose a capacitance array sensor and a hyper-heuristic partial differential equation (PDE) inversion method for detecting multiple micro-scale metal debris, combined with self-adaptive cellular genetic (SA-CGA) and morphological algorithms. Firstly, different from the traditional methods, which are limited in multi-induction-Dirac-boundary-inversion, a mathematical model with non-local boundary conditions is established. Furthermore, a hyper-heuristic method based on prior knowledge is also proposed to extract the wear character. Moreover, a 12-plate array circulating sensor and corresponding detection system are designed. The experimental results were compared with the optical microscopy. The results show that under the conditions of 1~3 wear debris with diameters of between 250⁻900 μm, the accuracy of the proposed method is 10⁻38% higher than those of the traditional methods. The recognition error of the wear debris counts decreases to 0. |
| first_indexed | 2024-04-11T21:34:53Z |
| format | Article |
| id | doaj.art-58dafb364b714a6ea98d2615daf7aa42 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:34:53Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-58dafb364b714a6ea98d2615daf7aa422022-12-22T04:01:48ZengMDPI AGSensors1424-82202019-01-0119351510.3390/s19030515s19030515Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris DetectionYanshan Sun0Lecheng Jia1Zhoumo Zeng2State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, ChinaOnline detection of fatigued wear debris in the lubricants of aero-engines can provide warning of engine failure during flight, thus having great economic and social benefits. In this paper, we propose a capacitance array sensor and a hyper-heuristic partial differential equation (PDE) inversion method for detecting multiple micro-scale metal debris, combined with self-adaptive cellular genetic (SA-CGA) and morphological algorithms. Firstly, different from the traditional methods, which are limited in multi-induction-Dirac-boundary-inversion, a mathematical model with non-local boundary conditions is established. Furthermore, a hyper-heuristic method based on prior knowledge is also proposed to extract the wear character. Moreover, a 12-plate array circulating sensor and corresponding detection system are designed. The experimental results were compared with the optical microscopy. The results show that under the conditions of 1~3 wear debris with diameters of between 250⁻900 μm, the accuracy of the proposed method is 10⁻38% higher than those of the traditional methods. The recognition error of the wear debris counts decreases to 0.https://www.mdpi.com/1424-8220/19/3/515wear debriscapacitance array sensorhyper-heuristic PDE inversionmorphological extract characterization |
| spellingShingle | Yanshan Sun Lecheng Jia Zhoumo Zeng Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection Sensors wear debris capacitance array sensor hyper-heuristic PDE inversion morphological extract characterization |
| title | Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection |
| title_full | Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection |
| title_fullStr | Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection |
| title_full_unstemmed | Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection |
| title_short | Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection |
| title_sort | hyper heuristic capacitance array method for multi metal wear debris detection |
| topic | wear debris capacitance array sensor hyper-heuristic PDE inversion morphological extract characterization |
| url | https://www.mdpi.com/1424-8220/19/3/515 |
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