To study the principle of loss and heat at the end region of large 4-poles nuclear power turbine generator, 3D transient electromagnetic field and 3D steady temperature field finite element (FE) models of the end region are established respectively. Considering the factors such as rotor motion, core non-linearity and time-varying of electromagnetic field, the anisotropic heat conductivity and different heat dissipation conditions of stator end region, a 50 Hz, 1150 MW, 4-poles nuclear power turbine generator is investigated. The loss and heat at the generator end region are calculated respectively at no-load and rated-load, and the calculation results are compared with the test data. The result shows that the calculation model is accurate and the generator design is suitable. The method is valuable for the research of loss and heat at the end region of large 4-poles nuclear power turbine generator and the improvement of the generator’s operation stability. The method has been applied successfully for the design of the larger nuclear power turbine generators.

Turbines and generators operating in the power generation industry are a major source of electrical energy worldwide. These are critical machines and their malfunctions should be detected in advance in order to avoid catastrophic failures and unplanned shutdowns. A maintenance strategy which enables to detect malfunctions at early stages of their existence plays a crucial role in facilities using such types of machinery. The best source of data applied for assessment of the technical condition are the transient data measured during start-ups and coast-downs. Most of the proposed methods using signal decomposition are applied to small machines with a rolling element bearing in steady-state operation with a shaft considered as a rigid body. The machines examined in the authors’ research operate above their first critical rotational speed interval and thus their shafts are considered to be flexible and are equipped with a hydrodynamic sliding bearing. Such an arrangement introduces significant complexity to the analysis of the machine behavior, and consequently, analyzing such data requires a highly skilled human expert. The main novelty proposed in the paper is the decomposition of transient vibration data into components responsible for particular failure modes. The method is automated and can be used for identification of turbogenerator malfunctions. Each parameter of a particular decomposed function has its physical representation and can help the maintenance staff to operate the machine properly. The parameters can also be used by the managing personnel to plan overhauls more precisely. The method has been validated on real-life data originating from a 200 MW class turbine. The real-life field data, along with the data generated by means of the commercial software utilized in GE’s engineering department for this particular class of machines, was used as the reference data set for an unbalanced response during the transients in question.