A fault diagnosis system based on case decision technology for UAV inspection of power lines

Abstract

Though Deep Learning CNN is mostly used in UAV power line-inspection system for the application of intelligent image recognition technology, can design image features easily and has strong adaptability to complex environments, but three problems deafly influence the actual results of application system such as insufficient image samples library, scarce labeling samples, and absent open-data source. To conquer these problems, CBR is proposed as a strategy for knowledge reasoning, which transform the similar case-space to a new situation for problem-solving, so the combination of RBR and CBR is expected to construct our flexible case- decision diagnosis system, which integrates efficient machine learning methods to give their full advantages to guarantee the good performance of the system for fault detection. The on spot experimental results indicates our system performs efficiently, assist people in decision-making and can find potential equipment faults.