Model-based Monitoring of Rotor Systems

对于转子的许多典型应用，例如涡轮机、压缩机和泵，安全性和可靠性起着重要作用，因为故障可能导致整个发电厂或飞机停机。然而，许多缺陷可以在它们变得严重之前被识别，因为它们在小的时候已经影响转子的行为。因此，状态监测可以用来寻找这些迹象，并警告所发生的损害。因此，很难指定错误的位置和数量以及转子的未来行为。当添加受损转子的仿真模型时，可以获得该信息。在本研究计画中，我们开发了一个自主式的模型监控系统。在执行安装阶段后，系统自动监控系统的振动。如果它们与正常操作不同，则将记录测量数据并与存储在损坏库中的典型错误模式进行比较。基于该错误分类的结果，开始适当的仿真以优化故障参数。通过优化的仿真，可以对转子的损伤进行详细的诊断。同时，损伤库包括不平衡、静态弯曲、角偏差和转子跌落引起的碰摩。在以后的检查中，应扩大损伤库。重点在于进一步的错位现象。它们尤其出现在具有多个轴承的转子中，就像发电厂中常见的那样。应研究在识别过程中如何将支座内的不对中分配给特定轴承，以及如何量化其数量。库的第二次扩展应包括刚性联轴器中的不对中。在这种情况下，未对准的部件与转子一起旋转，因此该误差具有与基座中的未对准不同的特性。实际上，它与转子的静态弯曲非常相似。离合器存在两种不同的不对中现象，即平面角度不对中和径向偏移，为了检测这两种不对中现象，必须对现有的试验台进行改造。还应该可以包括不同数量的误差。对于基于信号的故障分类，必须找到这些误差的典型模式，仿真中的误差模型应尽可能简单，以便快速识别误差，但必须正确描述误差特性。为了进一步验证，应测试多个设置，例如，测试台的小修改或几个错误的叠加。

For many typical applications of rotors, e.g. turbines, compressors and pumps, safety and reliability play an important role as failures could result in a shutdown of entire power plants or airplanes. Yet, many defects can be recognized before they get critical as they already influence the behavior of the rotor when they are small. So, condition monitoring could be used to search for these signs and to warn against the arising damage.Most of the currently applied monitoring systems only use measured data for diagnosis. Therefore, it is difficult to specify the location and amount of error and the future behavior of the rotor. This information can be gained when adding a simulation model of the damaged rotor. This is called the model-based monitoring.In this research project, an autonomous system for model-based monitoring is developed. After performing an installation phase, the system automatically monitors the vibrations of the system. If they differ from normal operation, the measurement data will be recorded and compared with typical patterns of errors which are stored in a damage library. Based on the results of this error classification, a suitable simulation is started in order to optimize the fault parameters. The optimized simulation allows a detailed damage diagnosis.Contemporary, the damage library includes unbalance, static bow, angular misalignment and rubbing due to rotor drops. In the following examinations, the damage library should be expanded. The focus lies on further misalignment phenomena. They especially occur in rotors with multiple bearings like they are common in power plants. It should be investigated how misalignment within the pedestal can be allocated to specific bearings during identification and how its amount can be quantified.The second expansion of the library should include misalignment in rigid couplings. In this case, the misaligned components rotate with the rotor, so this error has different characteristics than misalignment in the pedestal. In fact, it is quite similar to static bow of the rotor. There exist two kinds of misalignment in clutches, which are an angular mismatch between the plane faces and an offset in radial direction.In order to examine these failures, the existing test rig has to be updated. It should also be possible to include different amounts of the errors. For the signal-based fault classification, typical patterns for these errors must be found. The error models in simulation should be as simple as possible for fast error identification, yet they must describe the error characteristics correctly. For further verification, several setups should be tested, e.g. small modifications of the test rig or the superposition of several errors.