【摘要】 为提高双级滑阀真空泵工作可靠性,针对双级滑阀式机械真空泵在实际生产过程中可能出现的故障现象,引入控制律重组和PARD-BP神经网络故障诊断算法,以2H-150型双级滑阀真空泵振动故障样本采集数据作为输入,以故障模式矩阵作为目标输出,对PARD-BP算法所得数据进行训练优化;再提出动态数据信息提取概念,进行其动态信息收集提取,采用目前广泛应用的Poly-Max模态参数识别方法进行结果验证,结果表明真空泵X、Y和Z方向上最大的峰值分别下降了31.93%,21.54%和19.37%,证实滑阀真空泵的故障诊断方法具有可靠性。更多还原

【Abstract】 Novel fault-diagnosis algorithms were developed on the basis of control law reconfiguration and PARD-BP neural network to improve the reliability of the two-stage rotary piston vacuum pump installed in industrial production line.First,the optimized training of the data,collected in PARD-BP algorithm,was conducted with the sampled vibration faults of 2H-150two-stage rotary piston vacuum pump as the input and with the fault-mode matrix as the target output.Second,dynamic data information extraction concept was proposed to acquire the dynamic information.Finally,the optimized results were verified in the widely used Poly-Max modal parameter identification method.The novel algorithms were tested in experimental dynamic simulation.The test results show that the optimization resulted in reductions of the largest relative-displacement peaks in X,Y and Z directions of the vacuum pump by 31.93%,21.54% and 19.37%,respectively.We suggest that the fault diagnosis algorithms be of much technological interest.更多还原