基于数据挖掘技术的拖拉机发动机故障诊断

Tractor Engine Fault Diagnosis Based on Data Mining Technology

拖拉机是农业生产的重要工具,发动机是其核心部件,如发动机出现故障,将会直接影响农业生产效率和产量。为此,提出了一种使用数据挖掘技术进行拖拉机发动机故障诊断的方法。利用了机器学习技术和统计学,首先针对拖拉机田间运行信号噪音较大的问题,引入小波阈值去噪的方法;其次,基于卷积神经网络模型,引入一种注意力机制,提高故障诊断准确率,并通过对拖拉机传感器数据进行分析,可以帮助诊断和预测发动机故障;最后,通过实验结果验证了算法的有效性。研究结果不仅可以提高故障的准确性和效率,还能够节约维修成本和提高机器的利用率,具有较高的应用价值。

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 production.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 neural network model to improve the fault diagnosis accuracy,and it can help to diagnose and predict the engine fault by analyzing 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.