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Gas path fault diagnosis method and system for aeroengine dynamic process

An aero-engine and dynamic process technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as sensor signal redundancy, meet real-time requirements, reduce labor intensity, and save design and development costs Effect

Active Publication Date: 2018-07-06
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

However, a large number of sensor signals are redundant and accompanied by a lot of noise. In order to save computing resources and perform fault diagnosis effectively and quickly, it is necessary to perform feature extraction on the collected sensor time series

Method used

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  • Gas path fault diagnosis method and system for aeroengine dynamic process
  • Gas path fault diagnosis method and system for aeroengine dynamic process
  • Gas path fault diagnosis method and system for aeroengine dynamic process

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Embodiment Construction

[0048] The specific embodiment of the present invention will be described in further detail below in conjunction with the accompanying drawings:

[0049]The idea of ​​the present invention is to solve the problem of dynamic aeroengine sensor parameter redundancy and traditional diagnostic methods ignoring the time series correlation of parameters, and designs a dynamic aeroengine gas path fault diagnosis framework based on multi-layer kernel extreme learning machine-hidden Markov. The framework is divided into two parts: feature extraction and fault mode diagnosis. The feature extraction part uses a relatively novel multi-layer kernel extreme learning machine. Compared with the traditional principal component analysis and kernel principal component analysis, the multi-layer kernel extreme learning Kernel mapping, which projects the data into the high-dimensional kernel space in advance, and the multi-layer network structure greatly improves the feature extraction ability, while...

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Abstract

The invention discloses a gas path fault diagnosis method and system for aeroengine dynamic process. The method includes the steps of establishing a feature extraction model based on a multi-layer kernel extreme learning machine; adopting a hidden Markov model based on time series modeling for fault identification. The method solves the problem that the conventional data-based engine fault diagnosis uses time series measurement data to diagnose the fault with low accuracy in the existing aeroengine gas path fault diagnosis in the dynamic process, is suitable for the engine dynamic fault diagnosis in consideration of the degradation of gas path components and the redundancy of sensor parameters, and has a positive promotion effect on engine health management and maintenance cost reduction.

Description

technical field [0001] The invention belongs to the field of air path fault diagnosis of aero-engines, and in particular relates to a gas path fault diagnosis method and system for the dynamic process of an aero-engine. Background technique [0002] As the heart of an aircraft, an aero-engine has a complex structure and a harsh working environment. Engine fault diagnosis technology is an important means to ensure engine performance and reliability and reduce maintenance costs. During the service of an aero-engine, the performance of components will slowly degrade. In addition, sudden changes in component health parameters may occur; at the same time, due to the harsh working environment, sensors are also one of the components that are prone to failure; gas circuit component failure and sensor failure Together affect the performance and reliability of aero-engines, it is necessary to diagnose them. [0003] At present, the fault diagnosis of aeroengine air circuit components...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/15G06F30/20
Inventor 鲁峰蒋继鹏黄金泉仇小杰吴斌
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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