Aeroengine gas path fault diagnosis method based on twin support vector machine (SVM)

A support vector machine and aero-engine technology, applied in artificial life, biological models, special data processing applications, etc., can solve problems such as limited speed and accuracy of aero-engine gas path fault diagnosis

Inactive Publication Date: 2017-01-04
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0004] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a new method for diagnosing aeroengine gas path faults based on the twin support vector machine algorithm, which is used to solve the speed and accuracy of existing methods for diagnosing aeroengine gas path faults limited technical issues

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  • Aeroengine gas path fault diagnosis method based on twin support vector machine (SVM)
  • Aeroengine gas path fault diagnosis method based on twin support vector machine (SVM)
  • Aeroengine gas path fault diagnosis method based on twin support vector machine (SVM)

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with the accompanying drawings.

[0048] Such as figure 1 As shown, the implementation steps of the air path fault diagnosis of the aero-engine of the present invention are roughly as follows, using HPSO to optimize the TWSVM model, obtaining the optimal classification model by learning the fault data set, and then obtaining the target classification model, and then using the target classification model to distinguish Unknown type of failure.

[0049] Taking the gas path fault diagnosis problem of a certain type of turbofan engine as an example, the technical solution of the present invention will be described in detail in conjunction with the accompanying drawings:

[0050] Step 1. Use the engine modeling and simulation software GSP to establish a component-level simulation model of the engine, and then bring the thermodynamic parameters of each section under different working conditions into the pre...

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Abstract

The invention discloses a new method of gas path fault diagnosis of aeroengine based on hybrid particle swarm optimization twin support vector machine (SVM) algorithm. In view of the fact that the gas path fault is the most frequent in the whole aeroengine failure and the strong demand for the intelligent diagnosis method in the field, and TWSVM has the advantages of faster theoretical calculation and better response to the sample imbalance problem, and adopts the TWSVM algorithm to study aeroengine gas path fault diagnosis. In this paper, the hybrid kernel function is introduced to improve the performance of the kernel function, so that the TWSVM algorithm can better balance the generalization ability and good learning ability. In addition, the optimal parameters of TWSVM are optimized by using HPSO, and the fault classification model is obtained. The aeroengine gas path fault diagnosis is realized with high precision.

Description

technical field [0001] The invention belongs to the fault diagnosis technology of an aero-engine, and relates to the establishment of an aero-engine fault model, the acquisition of a fault diagnosis rule, a fault identification algorithm and an optimization algorithm of parameters thereof. Background technique [0002] The aeroengine is the heart of the aircraft, and its healthy operation is of great significance to ensure flight safety. Ensuring flight safety by technical means is an essential content in the aviation industry and has always been the top priority in aircraft design. According to statistics, engine failures account for a large proportion of aircraft failures and often cause catastrophic accidents. The cost of engine maintenance and replacement is huge, accounting for more than 60% of the regular maintenance costs of aircraft. Based on this, many well-known aviation scientific research institutions have been committed to developing technologies and devices t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06N3/00
CPCG06N3/006G16Z99/00
Inventor 杜彦斌肖玲斐胡继祥何虹兴
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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