FAULT PREDICTION MODEL OF CORN GRAIN HARVESTER BASED ON SELFCODING NEURAL NETWORK

Abstract

The corn grain harvester serves as an example of complex farming machinery with a condition monitoring system that collects a lot of working condition data, making it challenging to identify the true change pattern due to the data coming from the equipment in various states. Firstly, the overall structure of the corn grain harvester is analyzed, and the common causes and mechanisms of corn grain harvester failures are analyzed, leading to the cutting table as the main research object; Secondly, by collecting historical failure data of corn grain harvester as well as real-time failure information for collation and pre-processing, eliminating interference such as noise and missing data, establishing a failure matrix, extracting internal characteristics between failure causes and establishing a mapping between failure causes and failure phenomena; Finally, the future failure phenomena of the corn grain harvester are predicted according to different failure causes. The simulation analysis results show that the self-coding neural network fault prediction model can better predict the occurrence probability and types of faults and provide data support for fault maintenance and decision making of agricultural machinery.