Research On Monitoring And Fault Diagnosis Of The Emulation Pump

The emulation pump plays an important role in integrated mechanical excavate coal in colliery. Once serious accident occurred, it would do great loss to the corporation. So it has great value to take condition monitoring and fault diagnosis to the emulation pump in order to prevent accident and make the machine to work smooth. The vibration signal has plenty of information of machine, it is noncontact to the machine when the vibration is being collected, and so it is convenient to pick up the vibration signal when the machine is running. With PC, eddy current sensor and sound card, a vibration signal collecting system was sacredly designed. After the research of the valve's vibration, it was found that the fault signal's power in the low frequency domain is lower than the normal signal, In this paper, the method of fault features extraction is pointed out. The valve's vibration signal's six-frequency section's power in the low frequency domain is picked up as the fault feature. The artificial neural network can simulate the cause of human being's perceiving. It can learn from the training samples and adjust the network by itself. After learning from the training set, the network can automatically form the mapping of the relationship between the network's input and output. A new algorithm call PSO-BP optimal algorithm is presented in this paper by combining the particle swarm optimizer and network according to the nation of population entropy, so that people can make good use of the respective virtues. In order to solve the problem of the fault diagnosis of emulation pump, PSO-BP optimal algorithm is applied in this paper. The system of the fault diagnosis of emulation pump designed by visual basic 6.0 is displayed to users with friendly interface.The results indicate that the method used in feature extraction is effective; the PSO-BP optimal algorithm is capable of intelligent diagnosing. Both of them can be applied to the element's condition monitoring and fault diagnosis in other reciprocating machine.