Gas Turbine Engine Gas-path Fault Diagnosis Based on Improved SBELM Architecture

• Published in: International journal of turbo & jet-engines Vol. 35; no. 4; pp. 351 - 363
• Main Authors: Lu, Feng, Jiang, Jipeng, Huang, Jinquan
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 19.12.2018; Walter de Gruyter GmbH
• Subjects: 47.85.Gj; Aircraft; aircraft engine; Aircraft engines; Airplane engines; Bayesian analysis; Classifiers; Computer simulation; Diagnostic systems; extreme learning machine (ELM); Fault diagnosis; Gas turbine engines; gas-path fault diagnosis; Life cycle analysis; Life cycle assessment; Machine learning; measurement uncertainty; Model accuracy; Neural networks; Noise measurement; Performance degradation; sparse Bayesian; Uncertainty
• ISSN: 0334-0082; 2191-0332
• DOI: 10.1515/tjj-2016-0050

Various model-based methods are widely used to aircraft engine fault diagnosis, and an accurate engine model is used in these approaches. However, it is difficult to obtain general engine model with high accuracy due to engine individual difference, lifecycle performance deterioration and modeling uncertainty. Recently, data-driven diagnostic approaches for aircraft engine become more popular with the development of machine learning technologies. While these data-driven methods to engine fault diagnosis tend to ignore experimental data sparse and uncertainty, which results in hardly achieve fast fault diagnosis for multiple patterns. This paper presents a novel data-driven diagnostic approach using Sparse Bayesian Extreme Learning Machine (SBELM) for engine fault diagnosis. This methodology addresses fast fault diagnosis without relying on engine model. To enhance the reliability of fast fault diagnosis and enlarge the detectable fault number, a SBELM-based multi-output classifier framework is designed. The reduced sparse topology of ELM is presented and utilized to fault diagnosis extended from single classifier to multi-output classifier. The effects of noise and measurement uncertainty are taken into consideration. Simulation results show the SBELM-based multi-output classifier for engine fault diagnosis is superior to the existing data-driven ones with regards to accuracy and computational efforts.