Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

Kong, Changduk; Lim, Semyeong; Kim, Keunwoo

doi:10.1515/tjj-2012-0034

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Engine health management (EHM) plays a critical role to ensure the safety, reliability, and affordability of gas turbine engines, and it has been paid more attentions [3][4]. Gas path anomaly detection and fault diagnostics as the important parts of EHM provide an important basis for assessing engine health status and performance, developing a maintenance strategy [5].…”

Section: Introductionmentioning

confidence: 99%

Gas Path On-line Fault Diagnostics Using a Nonlinear Integrated Model for Gas Turbine Engines

Lü

Huang

Chen

et al. 2014

International Journal of Turbo &Amp; Jet-Engines

View full text Add to dashboard Cite

Gas turbine engine gas path fault diagnosis is closely related technology that assists operators in managing the engine units. However, the performance gradual degradation is inevitable due to the usage, and it result in the model mismatch and then misdiagnosis by the popular model-based approach. In this paper, an on-line integrated architecture based on nonlinear model is developed for gas turbine engine anomaly detection and fault diagnosis over the course of the engine's life. These two engine models have different performance parameter update rate. One is the nonlinear real-time adaptive performance model with the spherical square-root unscented Kalman filter (SSR-UKF) producing performance estimates, and the other is a nonlinear baseline model for the measurement estimates. The fault detection and diagnosis logic is designed to discriminate sensor fault and component fault. This integration architecture is not only aware of long-term engine health degradation but also effective to detect gas path performance anomaly shifts while the engine continues to degrade. Compared to the existing architecture, the proposed approach has its benefit investigated in the experiment and analysis.

show abstract

Section: Introductionmentioning

confidence: 99%