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针对工程机械中关键部件的故障特点,提出了工程机械故障控制理论和方法。通过对设备关键部件进行性能评估及剩余寿命预测,实施工程机械预维护,从而有效实现工程机械故障控制。首先介绍设备状态演化过程;然后提出工程机械故障控制方法及系统架构,其中RBF(Radial Basis Function,径向基)神经网络特征趋势预测为关键部件剩余寿命预测核心技术,以轴承振动趋势预测为对象,研究RBF神经网络趋势预测。试验结果表明其可行性和优越性,从而为整个系统的实现奠定基础。
Aimed at the fault characteristics of the key components in construction machinery, the theory and method of fault control of construction machinery are put forward. Through the performance evaluation of the key components of the equipment and the prediction of the remaining life, the implementation of construction machinery pre-maintenance, so as to effectively achieve the construction machinery failure control. Firstly, the evolution of equipment state is introduced. Then, the fault control method and system architecture of construction machinery are proposed. The feature trend prediction of radial basis function (RBF) neural network is the core technology for predicting the remaining life of key components. Bearing vibration trend prediction , Research RBF neural network trend forecast. The test results show its feasibility and superiority, so as to lay the foundation for the realization of the whole system.