针对航空电缆电弧故障引起的微小电流变化难以识别的问题，提出了一种基于Inception模块和双向长短期记忆网络（bidirectional long short-term memory, BiLSTM）的交流串联电弧故障诊断方法。首先通过计算自相关系数的离散平方和（discrete sum of squares of the atocorrelation coefficient）、信息熵（Shannon entropy）以及小波能量熵（wavelet energy entropy）提取原始电流数据的特征，将特征合并形成新的特征矩阵，对原始数据实现特征增强。之后Inception-BiLSTM网络利用特征矩阵进行学习，最后完成对电弧故障的诊断。为了验证模型在实际环境中的诊断性能，在充分考虑实际情况下，基于航空电缆电弧模拟实验平台进行了振动试验、应力实验以及潮湿电缆实验，并将实验数据整合作为检测样本。实验结果表明，本文方法对于识别电弧故障有着较高的准确度，可以达到99.69%。

A method for diagnosing AC series arc faults based on the Inception module and BiLSTM (bidirectional long short-term memory) was proposed to address the challenge of identifying small current changes caused by arc faults in aviation cables. First, features of the raw current data were extracted by calculating the discrete sum of squares of the autocorrelation coefficient, Shannon entropy, and wavelet energy entropy. These features are then combined to form a new feature matrix, enhancing the original data's feature representation. Subsequently, the Inception-BiLSTM network learns from the feature matrix and ultimately completes the arc fault diagnosis. To validate the diagnostic performance of the model in practical environments, a series of experiments were conducted, including vibration tests, stress tests, and wet cable tests, based on an aviation cable arc fault simulation platform, with the experimental data being integrated as detection samples. The experimental results show that the proposed method achieves a high accuracy rate of 99.69% in identifying arc faults.