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复杂机电装备运行状态直接体现了装备自身的稳定性与可靠性,对其运行状态的监测就成为了制造企业保证生产过程稳定可靠的一项关键技术。为了准确监测复杂机电装备的运行状态,利用遗传算法的全局优化特点确定出BP神经网络的初始权值阈值,结合复杂机电装备的特征参数和评估体系,建立了一种基于改进BP神经网络的运行状态预测模型,实现了对复杂机电装备运行状态的监测。最后,以汽车装配线的拧紧机设备为实验对象,验证方法的有效性。
The operational status of complex mechanical and electrical equipment directly reflects the stability and reliability of the equipment itself, and the monitoring of its operating status has become a key technology for manufacturing enterprises to ensure the production process is stable and reliable. In order to accurately monitor the running status of complex mechanical and electrical equipment, the initial weight threshold of BP neural network is determined by using the global optimization features of genetic algorithm. Combined with the characteristic parameters and evaluation system of complex mechanical and electrical equipment, an improved BP neural network based operation State prediction model, to achieve the monitoring of the operational status of complex mechanical and electrical equipment. Finally, taking the tightening machine equipment of automobile assembly line as the experimental object, the validity of the method is verified.