Application of extended isolation forest in avionics equipment fault diagnosis

Avionics equipment is composed of multiple layers and complex structure, so it is difficult to research progress based on fault mechanism, and the amount of effective fault data of the model is insufficient, and it is difficult for the general fault diagnosis algorithm to train fault data. In order to realize the fault diagnosis of avionics equipment, machine learning is applied to the fault diagnosis of avionics equipment. Research samples are selected from ground operation simulation data, and an algorithm based on the combination of feature selection and extended isolation forest is proposed to detect and categorize typical faults of electronic modules. It can be well applied to the actual fault detection of avionics equipment . It can meet the requirements of lightweight applications and has practical engineering value.