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To improve gas-path performance fault pattern recognition for aircraft engines, a new data-driven diagnostic method based on hidden Markov model (HMM) is proposed. A redundant sensor somewhat interferes with fault diagnostic results of the HMM, and it also increases the computational burden. The contribution of this paper is to develop an iterative reduced kernel principal component analysis (IRKPCA) algorithm to extract fault features from original high-dimension observation without large additional calculation load and combine it with the HMM for engine gas-path fault diagnosis. The optimal kernel features are obtained by iterative sequential forward selection of the IRKPCA, and the features with lower dimensions are contracted through a trade-off between the fault information and modeling data scale in reduced kernel space. The similarity degree is designed to simplify the HMM modeling data using fault kernel features. Test results show that the proposed methodology brings a significant improvement in diagnostic confidence and computational efforts in the applications of a turbofan engine fault diagnosis during its steady and dynamic process.

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.

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