The Research Of Mine Ventilator Running State Prediction

The coal mine ventilator is the mine workers’ breathing machine. Its spindlereliability influence the mine production and the safety of workers directly. It is animportant ventilative equipment. At present, mining equipment maintenance in ourcountry is developing lag behind. We always use the traditional and regular maintenance.It may cause the excessive or inadequate maintenance. The stable equipment may haveproblems by this maintenance, or cause a major accident by error operating. Thus a newway of maintenance, condition based-maintenance may be the direction of equipmentmaintenance. It doesn’t have specific maintenance cycles. This method can determine theoperation state of the machine and predict a future period of equipment’ running state bymonitoring the operating status of the equipment in real time and analyzing the historydata. According to the monitoring data, we can judge the different fault types and makedifferent maintenance measures. Therefore, this article has researched the predictionmethod of mine ventilator’s running state.This article has proposed the research of the mine ventilator’s running stateprediction through researching the fault mechanism of the mine ventilator. At the sametime the author has proposed the prediction method of the mine ventilator running stateprediction combing EMD with the neutral network considering the nonstationarity of themine ventilator’ vibration signal.Firstly, the author built a real-time data acquisition system of the mine ventilator anddesigned the hardware and software of the system by combining the sensor withcomputer technology. Secondly, the author built a data storage and management systemof the mine ventilator by combining the LabVIEW and the SQL database technology. Inthis way, the effective management of the Real-time collected data, fixed cycle data, faultdata, diagnosis result data and field technical personnel diagnosis and maintenance datahas been achieved. They can provide a complete history file for further analysis of themine ventilator running state. Finally, the author built a mine ventilator forecast modelbased on the EMD and neural network by combining the EMD and neural networktechnology. The author used the EMD to decompose mine ventilator signals by theMATLAB completed the comparative study on the method of direct neural networkprediction and the method of EMD-based neural network prediction. The results showthat the latter has better predictive accuracy.