The invention relates to a helicopter moving part anomaly detection method based on a generative adversarial network, and the method comprises the following steps: 1, preprocessing the health data of a helicopter; 2, designing and constructing a generative adversarial network; 3, performing unsupervised training of the generative adversarial network; 4, setting an alarm threshold value; 5, performing anomaly detection; 6, performing self-adaptive online adjustment of the threshold value. According to the method, massive helicopter moving part health data can be fully utilized, unsupervised learning is carried out on health data distribution by utilizing the generative adversarial network, the data utilization rate is improved, an anomaly detection alarm threshold value can be set without a large number of abnormal samples, and the requirement for data collection is reduced. In combination with continuously supplemented healthy and abnormal samples, an abnormal detection alarm threshold is adjusted online so as to adapt to abnormal detection requirements, reduce a false alarm rate and improve a detection rate.
Helicopter moving part anomaly detection method based on generative adversarial network