Most recently proposed techniques for inverse rotordynamic problems seek to identify the unbalance on a rotor using a known structural model and measurements from externally mounted sensors only. Such non-intrusive techniques are important for balancing rotors that cannot be accessed under operational conditions because of temperature or space restrictions. The presence of nonlinear bearings, like squeeze-film damper (SFD) bearings used in aero-engines, complicates the solution process of the inverse rotordynamic problem. In certain practical aero-engine configurations, the solution process requires a substitute for internal instrumentation to quantify the SFD journal vibration. This can be provided by an inverse model of the SFD bearing which outputs the time history of the relative vibration of the SFD journal relative to its housing, for a given input time history of the SFD force. This paper focuses on the inverse model of the SFD and presents an improved methodology for its identification via a Recurrent Neural Network (RNN) trained using experimental data from a purposely designed rig. The novel application of chirp excitation via two orthogonal shakers considerably improves both the quality of the training data and the efficiency of its generation, relative to an earlier preliminary work. Validation test results show that the RNNs can predict the journal displacement time history with reasonable accuracy. It is therefore expected that such an inverse SFD model would serve as a reliable component in the solution of the wider inverse problem of a rotordynamic system.
Improved Empirical Identification of the Inverse Model of a Squeeze-Film Damper Bearing Based on a Recurrent Neural Network
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Al-Ghazal, GG, Bonello, P, & Torres Cedillo, SG. "Improved Empirical Identification of the Inverse Model of a Squeeze-Film Damper Bearing Based on a Recurrent Neural Network." Proceedings of the ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. Volume 7A: Structures and Dynamics. Oslo, Norway. June 11–15, 2018. V07AT33A016. ASME. https://doi.org/10.1115/GT2018-75777
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