Application of Machine Learning in Simulation Models and Optimal Controllers for Fluid Film Bearings

Machine learning methods offer some alternatives to the conventional approaches to the development of passive and adjustable fluid film bearings. Data-based bearing models typically show an advantage over conventional numerical models in terms of computational speed, and can either replace or supplement them in certain applications. The most promising application of machine learning is to create high-performance models and optimal controllers for fluid film bearings. It covers a range of tasks connected with the rotor trajectory planning, like active vibration and friction reduction, that is the main scope of this work. On-line rotor position assessment considering the measured or estimated loads can also be implemented using fast data-driven models in diagnostics and predictive analytics systems. The work presents an analysis of this approach in terms of the accuracy of solutions, the time required for preparing data, and training the models. The results show that the calculation speed using data-driven models can be increased at least 10 times compared to the numerical models. Two ANN-based models with different structure were analyzed in accuracy and performance. A model consisting from three separate ANNs was introduced in addition to a single-ANN model based on the analysis of the bearing forces nonlinearities and demonstrated better accuracy and the training time reduced by 26%. The calculation speed increased 12 time compared to the reference numerical model. The use of approximation models is demonstrated for the case of active conical bearing with rotor motion control with intellectual DQN controller. Also the applicability of the approach is analyzed regarding the implementation of intellectual and predictive controllers of active bearings.