Design of online fault diagnosis and early warning system for coal mine rotating machinery
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摘要: 针对现有煤矿机械在线监测与诊断技术未实现故障特征在线提取及故障类型自动识别的问题,设计了一种基于LabVIEW的煤矿旋转机械故障在线诊断及预警系统。该系统采用频谱分析、功率谱分析、包络谱分析、倒频谱分析等方法分析振动信号,得到旋转机械运行过程中各部件的特征参数,与故障类型数据库里的特征参数进行对比,实现故障诊断。设计了精细诊断和粗略诊断2种故障诊断模式,通过互锁的方式将2种模式关联起来,若旋转机械各主要部件结构参数已知,可选用精细诊断模式,否则选用粗略诊断模式。通过模拟旋转机械转子不平衡故障验证系统性能,结果表明,该系统能够准确识别故障并发出提示,且操作简单、可靠性高。Abstract: In view of problems that existing coal mine machinery online monitoring and diagnosis technology did not realize online extraction of fault characteristics and automatic identification of fault types, an online fault diagnosis and early warning system for coal mine rotating machinery based on LabVIEW was designed. The system analyzes vibration signal by means of spectrum analysis, power spectrum analysis, envelope spectrum analysis and cepstrum analysis, and obtains characteristic parameters of each component in running process of the rotating machine, and compares it with characteristic parameters in fault type database to realize fault diagnosis. Two kinds of fault diagnosis modes are designed including fine diagnosis and rough diagnosis, the two modes are associated by interlocking. If structural parameters of the main components of rotating machine are known, the fine diagnosis mode can be selected,otherwise, the rough diagnosis mode can be selected. Performance of the system is verified by simulating rotor unbalance fault of rotating machine, and the results show that the system can accurately identify faults and issue prompts with simple operation and high reliability.
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Key words:
- coal mine rotating machinery /
- fault diagnosis /
- online diagnosis /
- online warning /
- fine diagnosis /
- rough diagnosis
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