Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks

Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagnosis, we cannot install too many sensors in the engine because the operating environ...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering 2016-12, Vol.17 (12), p.1287-1304
Main Authors: Feng, De-long, Xiao, Ming-qing, Liu, Ying-xi, Song, Hai-fang, Yang, Zhao, Hu, Ze-wen
Format: Article
Language:English
Subjects:
Belief networks
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Electrical Engineering
Electronics and Microelectronics
Engines
Entropy
Entropy (Information theory)
Fault diagnosis
Gas turbine engines
High temperature
Information technology
Instrumentation
Machine learning
Maintenance costs
Networks
Sensors
Service life
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Summary:Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagnosis, we cannot install too many sensors in the engine because the operating environment of the engine is harsh and the sensors will not work in high temperature, at high rotation speed, or under high pressure. Thus, there is not enough sensory data from the working engine to diagnose potential failures using existing approaches. In this paper, we consider the problem of engine fault diagnosis using finite sensory data under complicated circumstances, and propose deep belief networks based on information entropy, IE-DBNs, for engine fault diagnosis. We first introduce several information entropies and propose joint complexity entropy based on single signal entropy. Second, the deep belief networks （DBNs） is analyzed and a logistic regression layer is added to the output of the DBNs. Then, information entropy is used in fault diagnosis and as the input for the DBNs. Comparison between the proposed IE-DBNs method and state-of-the-art machine learning approaches shows that the IE-DBNs method achieves higher accuracy.
ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.1601365