应用改进粒子群算法的冶金机械机电设备故障检测方法

Fault detection method of metallurgical mechanical and electrical equipment based on Improved Particle Swarm Optimization

冶金机械机电设备故障检测的传统方法由于检测精度较低,往往不能将机电设备的故障全部检测出来,导致设备在故障的状态下继续工作,既损坏机械本身又降低机械的工作效率。因此提出应用改进粒子群算法的冶金机械机电设备故障检测方法这一课题。首先进行机电设备故障数据检测,基于建设数据进行机电设备故障诊断。设计对比实验,在相同的计算机仿真环境中,对比传统方法与应用改进粒子群算法的冶金机械机电设备故障检测方法检测准确率的高低,实验结果证明传统方法的检测效率不如新方法。

Due to the low detection accuracy,the traditional fault detection methods of metallurgical mechanical and electrical equipment often can not detect all the faults of the mechanical and electrical equipment,which leads to the continuous work of the equipment in the fault state,which not only damages the machinery itself,but also reduces the work efficiency of the machinery.Therefore,an improved particle swarm optimization algorithm for fault detection of metallurgical machinery and electrical equipment is proposed.Firstly,the fault data of mechanical and electrical equipment is detected,and the fault diagnosis of mechanical and electrical equipment is carried out based on the construction data.In the same computer simulation environment,a comparative experiment is designed to compare the detection accuracy of the traditional method and the fault detection method of metallurgical mechanical and electrical equipment based on improved particle swarm optimization algorithm.The experimental results show that the detection efficiency of the traditional method is lower than that of the new method.