石家庄煤矿风机运行状态的预测研究

 石家庄风机厂 石家庄风机 石家庄市风机厂 石家庄风机维修 石家庄风机销售

石家庄煤矿风机运行状态的预测研究
煤矿风机是矿井工作人员的呼吸机，其可靠性直接影响井下生产和工人的生命安全，是重要的通风设备。目前我国对矿山设备的维修很落后，大都采用传统的定期维修方式，这种维修方式会造成维修过剩或维修不足，其结果可能是原本稳定的设备，经过维修反而更易出现故障，或设备“带病”运行造成重大事故。所以一种新的维修方式—按状态维修成为设备维修的发展方向，这种维修的特点是，没有具体的维修周期，通过对设备运行状态的实时监测，以及历史数据的分析，判断机器设备的运转状态，并对未来某段时间的设备运转状态进行预测，根据监测数据判断不同的故障类型，制定不同的维修措施。为此，进行了煤矿通风机运行状态预测方法的研究。
论文通过对风机故障机理的研究提出了基于振动信号的风机运行状态的预测研究，通过对信号分析方法以及预测方法的归纳分析，同时考虑到风机振动信号的非平稳性，提出了 EMD 与神经网络相结合的风机运行状态预测方法。
论文将传感器技术与计算机技术相结合，构建了矿井风机振动数据的实时采集系统，完成了系统软硬件设计；将 LabVIEW 与 SQL 数据库技术相结合构建了矿井风机的数据存储与管理系统，实现对实时采集数据、定周期采集数据、故障数据、诊断结果数据以及现场技术人员诊断与维修数据的有效管理，为对风机运行状态作进一步分析提供完整的历史档案；将 EMD 与神经网络相结合构建了基于EMD 的神经网络风机运行状态预测模型，在 MATLAB 环境下实现了风机振动信号的 EMD 分解，完成了直接神经网络预测方法与基于 EMD 的神经网络预测方法的比较研究，结果表明后者有更好的预测准确性。
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