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Aircraft sensor Fault Diagnosis Method Based on Residual Antagonism Transfer Learning

机译：基于残留对抗转移学习的飞机传感器故障诊断方法

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With the rapid development of electronic information technology, aircraft has entered a completely electrified era, and the number of sensors has increased exponentially. Although key sensors have redundant designs, many aviation accidents in recent years are caused by sensor failures. Therefore, early detection of Aircraft sensor faults is of great significance for ensuring flight safety. Faced with a large number of unlabeled and uneven sample sensor data, a method for fault diagnosis of Aircraft sensors based on residual countermeasure migration learning is proposed. This method can help deep learning. The product neural network requires the limitation of a large number of labeled data, and uses the rich label data from different but related auxiliary fields to reuse and transfer the data of the target domain to achieve the purpose of transfer learning and realize the fault diagnosis of Aircraft sensors.
机译：随着电子信息技术的快速发展，飞机已进入完全电气化的时代，传感器的数量呈指数增加。 虽然关键传感器具有冗余设计，但近年来许多航空事故是由传感器故障引起的。 因此，飞机传感器故障的早期检测对于确保飞行安全性具有重要意义。 提出了一种面对大量的未标记和不均匀的样本传感器数据，提出了一种基于残余对策迁移学习的飞机传感器故障诊断方法。 这种方法可以帮助深入学习。 产品神经网络需要限制大量标记数据，并使用不同但相关辅助字段的丰富标签数据来重用和传输目标域的数据，以实现转移学习的目的，实现故障诊断 飞机传感器。

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