Aviation cables are prone to loose electrical connectors and broken wiring harnesses under vibration,leading to AC arc faults.A fault detection method for AC series arc faults based on generalized gini exponent and pulse neural network is proposed to address the issue of unclear frequency domain characteristics during fault detection.Firstly,it is proposed to use the generalized Gini index to analyze the current waveform of the test data.Secondly,the numerical difference between normal and fault cycles is determined.Then,compared with the time-domain characteristic indicators such as margin,kurtosis,and pulse factor,the proposed index is more accurate in identifying periodic faults in the current waveform.Finally,the generalized Gini index is transformed into eigenvalues and substituted into the LIF model for training,further improving the universality of the method.The experimental results indicate that this method can quickly and effectively detect aviation AC series arc faults.