Prediction of Blade Tip Timing Sensor Waveforms Based on Radial Basis Function Neural Network
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Published:2023-08-31
Issue:17
Volume:13
Page:9838
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Zhang Liang1ORCID,
Chen Cong1,
Xia Yiming1,
Song Qingxi1,
Cao Junjun1
Affiliation:
1. Faculty of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121004, China
Abstract
As the existing Blade Tip Timing (BTT) vibration measurement methods have serious under-sampling problems, where the blade resonance frequency is usually higher than the sampling frequency of the data acquisition system of the BTT method, resulting in large errors in the identification of blade vibration parameters, new solutions are needed to extend the capability of BTT to nonlinear and multimodal vibration analysis. Therefore, it is the current research direction to pursue new and more accurate measurement and signal processing methods. By analyzing the waveform data from the BTT sensor and using it for vibration analysis, it significantly extends the BTT database. To avoid the current problems of under-sampling and low recognition accuracy, this paper conducts a study on the recognition of rotating blade vibration parameters based on the Radial Basis Function (RBF) model by establishing a RBF neural network prediction model to analyze the static calibration experimental data and predict the waveform of the BTT sensor, and comparing the prediction curves of various models. As the results show, for the RBF model, the prediction accuracy is closely related to the source data of the sampling point data, when the source data predicted by the RBF model is close to the center of the samples, the prediction accuracy is high, meanwhile, the prediction accuracy decreases as it is far away from the center of these data. At the same time, the number of samples is too small to affect the prediction ability of the RBF model. By using this method, more waveforms under the Blade Tip Clearance (BTC) can be predicted with the available sample point data, and the errors in the experimental measurement process can be corrected.
Funder
National Natural Science Foundation of China
Liaoning Province Applied Basic Research Program Project
Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, North-eastern University
Liaoning Provincial Natural Science Foundation Guidance Program Project
Liaoning Provincial Department of Education Scientific Research General Project
Liaoning University of Technology Teacher Research Start Fund Project
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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