Tractor as an important tool for agricultural production,which,the engine is the core component of the whole machine,when the engine failure,will directly affect the efficiency and yield of agricultural production.Therefore,the timely diagnosis and repair of tractor engine faults is very important to ensure the smooth running of agricultural produc-tion.Therefore,this study proposes a method for tractor engine fault diagnosis using data mining techniques.The method utilizes the techniques of machine learning and statistics,firstly,it introduces a wavelet threshold denoising method for the problem of noisy tractor field operation signal,secondly,it introduces an attention mechanism based on convolutional neu-ral network model to improve the fault diagnosis accuracy,and it can help to diagnose and predict the engine fault by ana-lyzing the tractor sensor data.Finally,the effectiveness of the algorithm is verified by experimental results.The results of the study not only can improve the accuracy and efficiency of fault,but also can save the maintenance cost and improve the utilization rate of the machine,which has important practical significance and economic benefits.
维普
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