基于能耗参数的细纱机预维护系统设计OACSTPCD

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.