The present invention belongs to the field of digital information transmission technologies and discloses a fault detection method and system based on a generative adversarial network and a computer program. The fault detection method includes collecting samples and adding labels for the samples. A generative adversarial network is then trained to generate virtual fault samples, where the number of the generated virtual fault samples is equal to a difference between the number of normal samples and the number of fault sample. The virtual fault samples are added to the actually collected samples to obtain a new training data set. A classifier is then trained based on the new training data set, and fault detection and diagnosis is conducted using the trained classifier.
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