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Aviation dynamotor fault diagnosis method based on FWA-RNN model

A fault diagnosis and generator technology, applied in the direction of instruments, adaptive control, control/regulation systems, etc., can solve the problems of low automation, low analysis efficiency, slow convergence speed, etc., to achieve fast and accurate diagnosis, speed up convergence speed, Effects that improve speed and accuracy

Inactive Publication Date: 2018-07-03
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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AI Technical Summary

Problems solved by technology

At present, the commonly used fault diagnosis methods mainly adopt manual analysis or signal processing methods, and these methods have some shortcomings, such as low analysis efficiency, low degree of automation, etc.
[0004] Recursive neural network is a kind of neural network with fixed weights and thresholds. Because of the stability of its internal structure, it is easier to realize. It has the characteristics of simple principle, fast running speed, and strong local search ability. However, the initial parameter setting Improper use may cause it to converge slowly, or fall into a local minimum

Method used

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  • Aviation dynamotor fault diagnosis method based on FWA-RNN model
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  • Aviation dynamotor fault diagnosis method based on FWA-RNN model

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Embodiment Construction

[0023] The invention will be described in further detail below in conjunction with the accompanying drawings.

[0024] The present invention proposes a fault diagnosis method for aeronautical generators based on the FWA-RNN model. The method mainly includes fault mode and test signal analysis, signal acquisition, data preprocessing, using fireworks algorithm to optimize neural network initial parameters, and using recursive neural network Perform fault detection and performance detection. The process used is as figure 1 As shown, the specific operation includes the following steps:

[0025] First, determine the main fault types and numbers of aero-generators by consulting literature and experimental simulation. The main typical failure modes of aeronautical generators include internal short-circuit faults of stator windings, insulation faults of stator windings, inter-turn short-circuit faults of rotor windings, single-tube open-circuit faults, double-tube open-circuit fault...

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Abstract

The invention relates to an aviation dynamotor fault diagnosis method based on a FWA-RNN model, and belongs to the dynamotor state monitoring and fault diagnosis field; the method mainly comprises thefollowing steps: 1, analyzing fault modes and selecting test signals; 2, determining an aviation dynamotor to-be-tested signal, using sensors to gather mass data under various work states, and sending the data to a computer for data storage; 3, making FFT transformation for obtained mass data, carrying out normalization processing, and splitting obtained data into training samples and test samples; 4, combining test sample data, using FWA to optimize all initial parameters of a RNN, building a nerve network model, and inputting the training samples so as to make fault analysis; 5, detecting the model accuracy. The method has excellent data adaptive ability and robustness, and can effectively improve the aviation dynamotor fault classification detection efficiency and accuracy.

Description

technical field [0001] The invention relates to an aviation generator fault diagnosis method based on an FWA-RNN (Fireworks Algorithm-Recursive Neural Network) model, belonging to the field of generator state monitoring and fault diagnosis. Background technique [0002] The generator is the core of the power system. Once a failure occurs, it will not only threaten the stable operation of the power system, but also damage the generator equipment. With the continuous increase of stand-alone capacity, the operational reliability of generators is particularly important and prominent. Especially in the aviation power system, the normal operation of the brushless alternator has a decisive impact on the vitality of the aircraft. Therefore, it is of great significance to the development of the aviation industry to study and analyze the fault diagnosis of aero-generator equipment, to propose a more effective fault diagnosis system, and to better ensure the safety of aircraft operati...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 崔江刘力宇张卓然
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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