基于振动信号分析的联合收割机故障检测系统研究
Research on Fault Detection System of Combine Harvester Based on Vibration Signal Analysis
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摘要: 首先,介绍了振动信号分析原理及应用,并设计了联合收割机发动机振动信号采集与处理模块;然后,基于卷积神经网络,实现了联合收割机故障检测系统,并利用MatLab进行了发动机故障诊断仿真。实验结果表明:采用卷积神经网络的故障检测模型,可实现对联合收割机发动机转子、轴承和机匣的故障检测,且识别率在95%以上,证明了系统的可靠性和可行性。Abstract: It first introduces the principle of vibration signal analysis and its application, then designs the vibration signal acquisition and processing module of combine engine, finally realizes the combine fault detection system based on convolution neural network, and carries out the engine fault diagnosis simulation with MatLab. The experimental results show that the fault detection model of convolutional neural network can realize the fault detection of combine engine rotor, bearing and gearbox, and the recognition rate is more than 95%, which proves the reliability and feasibility of the system.