矿井主通风机在线监测与故障诊断系统

摘要

This paper aimed at the mine main fan, studied and analyzed its common failure types and performance characteristic, then determined the main monitoring parameters that can reflect the fan running state and performance. The paper constructed the hardware platform taking computer as the core, developed the corresponding monitoring and fault diagnostic procedures to a-chieve the fan parameters real-time monitoring and fault diagnosis afterward. The fan vibration signals was took as a diagnostic basis , application of wavelet packet decomposition technique to extracted the fan different fault characteristic information from different frequency bands, then took the fault characteristic information as the BP network fault samples, established the fault intelligent diagnosis system. The research has an important practical significance in improving the safety of mine fan operation, accuracy of fault diagnosis and ensures coal mine safety and high efficiency production.%文中以矿井主通风机为对象,对其常见故障类型、表现特征进行了研究和分析,确定了可反映风机运行状态和性能的主要监测参数.构建了以计算机为核心的硬件平台,设计开发了相应的监测和故障诊断程序,实现了通风机的重要参数的实时监测和常见故障诊断.故障诊断采用了以通风机的振动信号为诊断依据,利用小波包分解技术从不同频带中提取故障特征信息,作为BP网络的故障样本,建立故障智能诊断系统的方法.该研究对提高矿井通风机运行的安全性和故障诊断的准确性,保证煤矿安全高效生产具有重要的现实意义.