Remote Fault Diagnosis Model of Tractor Electrical System
Tractors are developing towards intelligence and automation,the limitations of traditional fault diagnosis methods are becoming increasingly prominent.Traditional fault diagnosis methods are limited by the lack of technical level and knowledge reserve,and usually rely on manual observation and experience judgment.To further improve the efficiency of tractor fault diagnosis,this study adopted a CNN neural network model to achieve automatic identification and classifica-tion of faults by training and learning from sensor data of tractor electrical systems.With a large number of data samples and remote data transmission,the model can perform fault diagnosis in a remote environment and give corresponding solu-tions.The test results showed that the model showed highly accuracy and efficiency in tractor electrical system fault diag-nosis,with 98.96%fault diagnosis accuracy,and had potential for practical application.The research results aim to pro-vide a new fault diagnosis method for the development of tractor intelligence and automation,and provide an important technical support for agricultural machinery maintenance.
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