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.
Highlights
Aeroengine is known as “the pearl on the crown of industry” because of the irreplaceable roles it plays in industry and the highly sophisticated internal structure it has
Diagnose Method Based on Multimodal Deep Neural Networks
BPNN is a multilayer fully connected neural network trained by the back propagation algorithm (BP), which is established by simulating the indescribable complicated working process in the brain
Summary
Aeroengine is known as “the pearl on the crown of industry” because of the irreplaceable roles it plays in industry and the highly sophisticated internal structure it has. This paper proposes a multimodal deep neural network diagnosis method based on the feature perception ability of CNN and the fitting ability of BPNN. The BPNN is employed to fit the sample data distribution, and CNN is utilized to explore the features of the samples, so as to obtain the multimodal decision information learned from different angles [11]. Experiments on several UCR standard time series datasets and the aeroengine fault datasets prove that by integrating the complementary information of multimodal neural networks which have distinctive abilities and learn sample data from different angles, a high accuracy diagnosis model can be achieved.
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