A noval intelligent aircraft cable fault detection based on deep neural network

The problem of cable fault detection will greatly affect the safety of aircraft operation, requiring timely detection and handling by staff. However, the problem of aircraft cable faults relies more on manual detection methods and traditional offline testing methods, and the amount of voltage data under normal operating conditions will be much larger than the fault data. Direct detection using existing deep learning models cannot achieve good results. To surmount this challenge, we present a noval model, annotated as CFDDR: Algorithm Based on Deep Learning and Cable Signal Reduction. CFDDR consists of three basic components: an aircraft cable data balancing module, a feature extraction module, and a deep neural network module. This method has excellent performance in detecting aircraft cable faults.