Aero engine gas-path fault diagnose based on multimodal deep neural networks

Abstract

Aeroengine, served by gas turbine, is a highly sophisticated system. It is a hard task to analyze the location and cause of gas-path faults by computational-fluid-dynamics software or thermodynamic functions. Thus, artificial intelligence technologies rather than traditional thermodynamics methods are widely used to tackle this problem. Among them, methods based on neural networks, such as CNN and BPNN, cannot only obtain high classification accuracy but also favorably adapt to aeroengine data of various specifications. CNN has superior ability to extract and learn the attributes hiding in properties, whereas BPNN can keep eyesight on fitting the real distribution of original sample data. Inspired by them, this paper proposes a multimodal method that integrates the classification ability of these two excellent models, so that complementary information can be identified to improve the accuracy of diagnosis results. Experiments on several UCR time series datasets and aeroengine fault datasets show that the proposed model has more promising and robust performance compared to the typical and the state-of-the-art methods.