矿用通风机故障诊断系统设计

Design of mine ventilator fault diagnosis system design

针对矿用通风机常见故障展开研究,分析了转子不对中、不平衡、油膜涡动、喘振等故障的产生机理及故障表征,设计了基于粗糙神经网络的故障诊断系统。首先针对通风机故障类型特点进行故障数据采集,包括振动信号和温度信号。然后,预处理后的样本数据采用粗糙集的方法进行属性约简,删除冗余属性。最后,样本数据被分成训练样本和测试样本,分别用来训练和测试神经网络分类机。实验表明,该系统运行可靠、诊断率高,提高了通风机系统的安全性,拓展了粗糙集的应用范围。

The common faults of mine ventilator are researched in this paper, and rotor misalignment, unbalance, oil whirl, surge and other faults and fault characterization of the generation mechanism are analyzed. The faults diagnosis system is designed based on rough neural network. First, the characteristics of the type of fault for fan failure data collection, including vibration and temperature signals. Then, the pretreated sample data using rough set attribute reduction method to delete redundant attributes. Finally, the sample data is divided into training and testing samples, were used to train and test the neural network classifier. Experiments show that the system is reliable, diagnostic yield, improved ventilator system security, expanding the scope of application of rough sets.