| Citation: | WANG Guan, WU Xianwei, QIAN Zhi, et al. A method for identifying bearing lubricating oil multi-debris based on two-level neural network[J]. Journal of Aerospace Power, 2025, 40（7）：20230745 doi: 10.13224/j.cnki.jasp.20230745
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An innovative method for identifying multiple debris in lubricating oil based on back propagation neural networks was introduced to address the challenge of multi-debris signal overlap. A two-level model framework was proposed, of which the first level network can accurately estimate the number of small debris in overlapping signals, and the second-level network utilized this quantity information to precisely analyze the diameter of these small debris, successfully overcoming the challenges posed by signal overlap. Through sufficient data training and model structure optimization, the model achieved 98.10%, 91.42%, and 93.06% accuracy, respectively, in single, double, and triple debris recognition.
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