Depth calculation model for aero-engine gas circuit fault diagnosis

Abstract

The invention discloses a deep calculation model for aero-engine gas circuit fault diagnosis, which belongs to the technical field of computers and comprises the following steps: (1) carrying out segmentation preprocessing on continuous aero-engine performance data; (2) extracting spatial features of the data object blocks by using a convolutional neural network; (3) learning local nonlinear features in the data object block by using a multilayer perceptron; (4) extracting long-term dependence features in the data object blocks by using a recurrent neural network; and (5) training the deep hybrid model by using an end-to-end method, i.e., combining an error back propagation algorithm. According to the embodiment of the invention, characteristics of an aero-engine model are considered, spatial characteristics, nonlinear characteristics and long-term dependence characteristics of gas path performance data generated by the aero-engine model are fused, an aero-engine gas path fault diagnosis model is constructed, and a joint error back propagation algorithm is designed for end-to-end training of a deep hybrid model by taking a classification task as a target.

Description

technical field [0001] The invention belongs to the technical field of computers and relates to an in-depth calculation model used for diagnosis of aeroengine gas circuit faults. Background technique [0002] The aero-engine is the power core of the aircraft, and its structure is complex. The harsh working environment is the main source of failure of the aircraft, and the performance status of the engine is directly related to the safety of the aircraft during flight. Engine fault diagnosis technology is an important means to prevent the occurrence of faults. It can eliminate various safety hazards in time. It is an important guarantee to ensure safe flight and is of great significance to improve the safety of aircraft flight. [0003] The structure of the aero-engine is complex, the mathematical model is severely nonlinear, and the monitoring data changes between multiple fault types are highly similar. At the same time, measurement errors are likely to interfere with the ...

AI Technical Summary

Problems solved by technology

However, the existing fault diagnosis methods for aviation engines are only aimed at a certain type of faults, and do not have certain universality.

In addition, the use of a single model for fault diagnosis results in poor anti-interference, but the combination of multiple models for fault diagnosis has problems such as complex

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