Hyper elite ultra condensed font5/1/2023 ![]() ![]() The causes are related to material, design, environment, and working conditions. Wear of gears, bearings and other components of aero-engines is the main mechanical factor leading to engine failure and major accidents. The recognition error of the wear debris counts decreases to 0. 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 experimental results were compared with the optical microscopy. Moreover, a 12-plate array circulating sensor and corresponding detection system are designed. Furthermore, a hyper-heuristic method based on prior knowledge is also proposed to extract the wear character. ![]() 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. 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. 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. ![]()
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