Adaptive fault detection method for airplane rotation actuator driving device based on deep learning

Abstract

The invention discloses an adaptive fault detection method for an airplane rotation actuator driving device based on deep learning. According to the invention, adaptive fault detection is carried out on the airplane rotation actuator driving device based on a sparse Dropout automatic coder and a noise reduction automatic coder and deep learning of Logistic regression, feature self-learning of original data is realized through using the Dropout automatic coder in a first layer and a layered noise reduction automatic coder model in a second layer and a third layer by adopting a multi-layer neural network based deep learning autonomous cognitive method, data features acquired by learning are inputted to a Logistic regression model so as to judge an operating state of the rotation actuator driving device, a threshold is enabled to change along with different inputs and different states of the system through additionally arranging an adaptive threshold fault observer, and a residual error caused by non faults is eliminated. The method disclosed by the invention can be effectively applied to fault diagnosis of the airplane rotation actuator driving device.

Description

technical field [0001] The invention relates to an adaptive fault detection method for an aircraft rotary actuator drive device based on deep learning, and belongs to the technical field of fault detection. Background technique [0002] The rotary actuator system has the advantages of large speed / mass ratio, simple and compact structure, and fast dynamic response, and has been widely used in aircraft, ships, and tanks. The drive device is an important part of the rotary actuator, and the failure of the drive device of the aircraft rotary actuator will affect the safe and stable operation of the entire aircraft, causing huge economic losses and even unpredictable consequences. Therefore, it is of great significance to ensure the normal operation of the aircraft rotary actuator drive device in practical applications. [0003] In the research on the fault detection technology of the control system of the aircraft rotary actuator drive device in recent years, the fault detectio...

AI Technical Summary

Problems solved by technology

[0006] The technology of the present invention solves the problem: in order to overcome the influence of system nonlinear factors on the fault detection method based on the observer, combined with the advantages of deep learning and efficient complex nonlinear function fitting and approximation, a deep learning-based aircraft rotary actuator drive is proposed The device adaptive fault detection method minimizes the influence of nonlinear factors on the residual and enhances the effect of faults on the residual as much as possible. The observer constructed by the deep learning regressor is used to generate residual information. Through the deep learning regressor Establish an adaptive threshold network to generate adaptive thresholds. The adaptive thresholds change with changes in the working state and environment of the control system, so as to realize self-adaptive judgment of whether the control system is faulty.

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