Engine gas path component fault diagnosis based on a sparse deep stacking network

Accurate engine gas path component fault diagnosis methods are key to ensuring the reliability and safety of engine operations. At present, the effectiveness of the data-driven gas path component fault diagnosis methods has been widely verified in engineering applications. The deep stack neural network (DSN), as a common deep learning neural network, has been gaining more attention in gas path fault diagnosis studies. However, various gas path component faults with strong coupling effects could occur simultaneously, resulting the DSN method less effective for engine gas path fault diagnosis. In order to improve the prediction performance of the DSN handling multiple gas path component fault diagnosis, a sparse regularization and representation method was proposed. The sparse regularization term is used to expand the traditional deep stacking neural network in the sparse representation, and the predicted output tag is close to the target output tag through this term. The diagnosis performance of six different neural network methods were compared by various engine gas path component fault diagnosis types. The results show that the proposed sparse regularization method significantly improves the prediction performance of the DSN, with an accuracy rate 99.9% under various gas path component fault conditions, which is higher than other methods. The proposed engine gas path component fault diagnosis method can handle multiple coupling gas path faults, and help engine operators to develop maintenance plans for the purpose of engine health management.