Abstract
The goal of this work is association of several machine learning methods in a study of rotating machines with fluid-film bearings. A fitting method is applied to fit a non-linear reaction force in a bearing and solve a rotor dynamics problem. The solution in the form of a simulation model of a rotor machine has become a part of a control system based on reinforcement learning and the policy gradient method. Experimental part of the paper deals with a pattern recognition and fault diagnosis problem. All the methods are effective and accurate enough.
Highlights
The main tool in modern machine learning is an artificial neural network (ANN) [1]
Deep learning is emerging in reinforcement learning and continuous control systems [7, 8]
The results demonstrated that the rotor dynamics simulation program with the ANN module allows calculation rotor trajectory two times faster than a real time process
Summary
The main tool in modern machine learning is an artificial neural network (ANN) [1]. This work deals with applications of machine learning to rotating machines with fluid-film bearings. This part of the rotor dynamics problem can be implemented using ANNs [4]. Modern rotating machines can be equipped with a number of sensors. Analysis of their measurements can be automated using specialized ANNs. Analysis of their measurements can be automated using specialized ANNs These ANNs implement logistic regression [1]. This paper unites theoretical and experimental results achieved by the authors in applications of machine learning to simulation, diagnosis and control of rotating machines with fluid-film bearings
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
