Fault Prediction Method of the Marine Gas Turbine Based on Neural Network-Markov

Author:

Cui Jian Guo1,Zhang Long1,Wang Gui Hua2,Cui Bo2,Jiang Li Ying1

Affiliation:

1. Shenyang Aerospace University

2. Shenyang Aero-Engine Design and Research Institute

Abstract

Since the fault of marine gas turbine is difficult to predict accurately, making the rolling bearing as the specific object, a fault prediction model of the marine gas turbine based on Neural Network and Markov method is built through the data analysis, preprocessing and feature extraction for the rolling bearing history test data. First, it uses the neural network method to realize the health state recognition of the marine gas turbine. Then, the fault of the marine gas turbine is predicted by taking advantage of the fault prediction which is based on the Markov model. The results show that the efficiency of fault prediction for the marine gas turbine can be realized better through the fault prediction model constructed in view of the Neural Network and Markov. And it also has a significant practical value in project item.

Publisher

Trans Tech Publications, Ltd.

Reference7 articles.

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3. Ming Dong. Hidden Semi-Markov Model-based Methodology for Multi-senor Equipment Health Diagnosis and Prognosis [J]. European Journal Operational Research, 2006: 858~878.

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