基于能耗参数的细纱机预维护系统设计

Design of pre-maintenance system for spinning frame based on energy consumption parameter

针对目前细纱机周期性维护方法的缺点,设计并开发了一种基于能耗参数的预测性维护系统。该系统利用传感器技术实现细纱机能耗数据的实时采集;通过基于BP和PSO-BP两种不同神经网络的预测模型试验对比,选取基于PSO-BP神经网络故障预测模型,对细纱机在不同故障下的能耗参数变化趋势进行预测;最后基于B/S架构开发了基于能耗参数的预维护系统,实现了细纱机的预测性维护。结果表明:该系统的应用优化了细纱机的维护管理,降低了细纱机维护成本。认为:该系统可分析细纱机未来运行状态,达到了预测细纱机故障的目的。

Aiming at the shortcomings of the current spinning frame periodic maintenance methods,a predictive maintenance system based on energy consumption parameters was designed and developed.Sensor technology was utilized in the system to achieve real-time collection of energy consumption data for spinning machines.By comparing the prediction models based on two different neural networks,BP and PSO-BP,a fault prediction model based on PSO-BP neural network was selected to predict the trend of energy consumption parameters changes of spinning frames under different faults.Finally,a pre maintenance system based on energy consumption parameters was developed based on B/S architecture,which realized the predictive maintenance of spinning frame.The results indicated that the system application has optimized the maintenance management of the spinning frame and reduced the maintenance cost of the spinning frame.It is considered that the system can be used to analyze the future running state of spinning frame and to predict the faults of spinning frame.