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• W2912802247 abstract "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." @default.
• W2912802247 created "2019-02-21" @default.
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• W2912802247 date "2019-01-26" @default.
• W2912802247 modified "2023-10-16" @default.
• W2912802247 title "Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection" @default.
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• W2912802247 doi "https://doi.org/10.3390/s19030515" @default.
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