大型机组振动故障的早期预警技术

状态检修是电力设备检修方式的未来发展趋势,对特征信息微弱的或处于萌芽状态的早期故障做出判断是状态检修的关键。为此,通过灰色理论和相似性原理对大型机组的实时信息数据(PI数据)进行开发,建立了若干台汽轮发电机组的动态预警模型,研发了设备振动故障的早期预警功能,所开发的超球相似度分析技术,为设备健康状态的智能监测和评估提供了新方法。应用该预警系统发现了机组的异常状态,实际应用验证了所开发的振动故障早期预警技术的有效性。

Vibration Fault Early Warning Technology for Large Turbine Generator Sets

As the future development trend of power equipment maintenance mode will be condition based maintenance, making judgment of early fault with weak feature-information or in the bud is the key to condition based maintenance. Therefore, the real-time information data(PI data)of large units was developed to build dynamic early warning models for several turbine generator units through the grey theory and similarity principle, realizing the early warning function of equipment vibration fault. A new method of intelligent monitoring and evaluation on the health state of equipment was provided by the hypersphere similarity analysis technology. And an abnormal state of the unit was discovered by this warning system as proof of the early warning function of vibration fault in large turbine generator sets in actual application.