Session: 05-16 Instrumentation IV: AI-based Improvements

Paper Number: 124927

124927 - Prediction of High-Speed Hydraulic Dynamometer Safety Envelope Base on Deep Learning Neural Network 

High-speed hydraulic dynamometer is widely used for turbine component experiment in aircraft engine area. It usually works through measuring main performance parameters of turbine for verifying design method by absorbing huge shaft work which is passed from turbine. However, its structure may cause several problems that decrease operation time. On the one hand, water flow can generate strong turbulence phenomena between impellers in dynamometer’s case. On the other hand, water temperature raises with the increase of absorbed work, it may generate cavitation phenomenon and cause high-frequency noise and pressure pulsation. Both can lead to dynamometer’s performance degradation.

Artificial experience diagnosis, though not suggested, is used commonly to prevent high-speed hydraulic dynamometer from working unsteadily. This method depends on the experience of workers, which may cause fuzzy definition and lead to safety hazard. Recent years, modeling method based on neural networks grow rapidly.

In this paper, we propose a performance model of high-speed hydraulic dynamometer based on deep learning neural network. It can draw work safety envelope by predicting performance parameters that delimit the safety boundary. Follow this guide workers can be able to make operation safer and stabler. We also propose a degradation model of work safety envelope, which does help to correct the envelope during continuous using of high-speed hydraulic dynamometer.

Presenting Author: Guo Chen Northwestern Polytechnical University

Presenting Author Biography: A Ph.D. Candidate at School of Power and Energy, Northwestern Polytechnical University.
Education background:
From September 2018 to March 2022, the author studied at the School of Power and Energy of
Northwestern Polytechnical University and obtained a bachelor's degree in aircraft engine
engineering;
Research interests: Deep learning; Simulation on the performance of aircraft engine.

Authors:

Guo Chen Northwestern Polytechnical University
Hong Xiao School of Power and Energy, Northwestern Polytechnical University
Li Zhou School of Power and Energy, Northwestern Polytechnical University
Rui You School of Power and Energy, Northwestern Polytechnical University