診断のための回転機械の物理モデル化

抄録

To achieve Condition-Based Maintenance through IoT, it is very important to catch the symptom of faults. As a method of grasping the operating states with faults, it is conceivable to collect operation data by embedding the damaged part into the actual machine. If the equipment is large, the lifetime is long, or the equipment is expensive, it is difficult to accumulate data. Hence, it is important to carry out analytical investigations in order to gain some insight into diagnostics. In this research, we focused on rotating machinery. We created the rotating machinery library by the equivalent method as the transfer matrix method in Modelica. We described a modeling method for rotating machinery in Modelica. To validate our models, we compared both Modelica simulation and experiment with a rotor kit as a test case.