In order to remotely monitor and maintain large-scale complex equipment in real time, China Telecom
plans to create a total solution that integrates remote data collection, transmission, storage, analysis and prediction.
This solution can provide manufacturers with proactive, systematic, integrated operation and maintenance
service, and the data analysis and health forecasting are the most important part. This paper conducts health
management for the turbine blades. Elman neural network, and improved Elman neural network, i.e., outputhidden
feedback (OHF) Elman neural network are studied as the main research methods. The results verify the
applicability of OHF Elman neural network.
ZHUO Pengcheng (卓鹏程), ZHU Ying (朱颖), WU Wenxuan (邬雯喧), SHU Junqing (舒俊清), XIA Tangbin (夏唐斌)
. Real-Time Fault Diagnosis for Gas Turbine Blade Based on Output-Hidden Feedback Elman Neural Network[J]. Journal of Shanghai Jiaotong University(Science), 2018
, 23(Sup. 1)
: 95
-102
.
DOI: 10.1007/s12204-018-2028-4
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