Abstract
Abstract In order to solve the difficult problem of fault diagnosis in aviation equipment, this paper uses a biomimetic pattern recognition method. Compared to traditional pattern recognition, biomimetic pattern recognition is able to construct closed, complex geometries to cover all kinds of samples. Biomimetic pattern recognition is a “cognitive” approach, where the process between two similar things is considered asymptotic. It mathematically means that all similar samples in the feature space are continuously indistinguishable. The paper establishes a dual-weighted neural network model of an aircraft auxiliary power unit. Comparing the diagnostic process and results with traditional manual neural networks. The results show that biomimetic pattern recognition is a feasible and efficient diagnostic method for the fault diagnosis of aviation equipment. It provides a new way of thinking and method for the practical application of aviation equipment fault diagnosis.
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