石家莊煤礦風機運行狀態的預測研究

 石家莊風機廠 石家莊風機 石家莊市風機廠 石家莊風機維修 石家莊風機銷售

石家莊煤礦風機運行狀態的預測研究
煤礦風機是礦井工作人員的呼吸機，其可靠性直接影響井下生產和工人的生命安全，是重要的通風設備。目前我國對礦山設備的維修很落后，大都采用傳統的定期維修方式，這種維修方式會造成維修過剩或維修不足，其結果可能是原本穩定的設備，經過維修反而更易出現故障，或設備“帶病”運行造成重大事故。所以一種新的維修方式—按狀態維修成為設備維修的發展方向，這種維修的特點是，沒有具體的維修周期，通過對設備運行狀態的實時監測，以及歷史數據的分析，判斷機器設備的運轉狀態，并對未來某段時間的設備運轉狀態進行預測，根據監測數據判斷不同的故障類型，制定不同的維修措施。為此，進行了煤礦通風機運行狀態預測方法的研究。
論文通過對風機故障機理的研究提出了基于振動信號的風機運行狀態的預測研究，通過對信號分析方法以及預測方法的歸納分析，同時考慮到風機振動信號的非平穩性，提出了 EMD 與神經網絡相結合的風機運行狀態預測方法。
論文將傳感器技術與計算機技術相結合，構建了礦井風機振動數據的實時采集系統，完成了系統軟硬件設計；將 LabVIEW 與 SQL 數據庫技術相結合構建了礦井風機的數據存儲與管理系統，實現對實時采集數據、定周期采集數據、故障數據、診斷結果數據以及現場技術人員診斷與維修數據的有效管理，為對風機運行狀態作進一步分析提供完整的歷史檔案；將 EMD 與神經網絡相結合構建了基于EMD 的神經網絡風機運行狀態預測模型，在 MATLAB 環境下實現了風機振動信號的 EMD 分解，完成了直接神經網絡預測方法與基于 EMD 的神經網絡預測方法的比較研究，結果表明后者有更好的預測準確性。
Abstract
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.Figure 56； Table 22； Reference 60Keywords: condition monitoring, LABVIEW, SQL, Hilbert-Huang analysis, NeuralNetworksChinese books catalog: TH17