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Aircraft engine transient state fault detecting method

An aircraft engine, fault detection technology, applied in neural learning methods, computer parts, instruments, etc.

Active Publication Date: 2015-05-06
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0005] In order to solve the problem that the existing aircraft engine fault detection is only limited to collecting engine data in a steady state, and then perform fault detection, the present invention proposes a method for aircraft engine transient fault detection, using Markov adaptive Hessian local linear A Method Combining Mahalanobis Adaptive Hessian Locally Linear Embedding (MAHLLE) and Adaptive BP Neural Network to Realize the Detection of Transient Faults of Aircraft Engines

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Embodiment Construction

[0045] Below in conjunction with accompanying drawing, content of the present invention is described in further detail:

[0046] Transient fault detection methods can be used to detect a variety of faults that occur under transient conditions. One area of ​​particular application is the start-up moment of the turbine engine. Other areas also include transients from engine ground idle to maximum speed flight and from flight idle to cruise. as attached figure 1 Shown is a diagram illustrating the rotational speed of a typical turbine engine during start-up. figure 1 The engine speed normalized value is illustrated as a function of time normalized at startup, with a maximum value of 20 seconds on the time axis. The sequence during start-up is as follows: The start-up process begins when the engine compressor is turned and the ignition switch is turned on, causing the igniter spark plug to fire. At approximately 10% of full engine speed, or 0.1, the ignition system is turned o...

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Abstract

The invention provides an aircraft engine transient state fault detecting method which includes first extracting marked features from rotating speed data of an aircraft engine based on a Mahalanobis adaptive Hessian locally linear embedding feature extractor; then utilizing an adaptive BP neural network classifier to analyze and determine whether faults are generated in the transient state according to the result of the feature extraction; finally processing the detected faults through a diagnosis system to generate a maintenance guide. The method solves the problem that the existing aircraft fault detection is only confined to engine data collection in the steady state. The method adopts combination of the Hessian locally linear embedding (Mahalanobis adaptive Hessian locally linear embedding) and adaptive BP (Back Propagation) neural network. The method can effectively improve the fault detection performance of the aircraft engine in the transient state.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a method for detecting a transient fault of an aircraft engine. Background technique [0002] The increasing complexity of modern aircraft has led to an increased need for systems that can automatically detect aircraft failures. These fault detection systems are designed to monitor the status of the aircraft in these systems to detect potential faults, so that potential faults can be dealt with before they can cause more serious system failures, such as in-flight shutdown, take-off Suspension, delay or cancellation of flights, etc. [0003] The engine is a particularly important part of the aircraft, so the fault detection method of the aircraft engine is an important part of the aircraft fault detection system. Traditional engine fault detection is only limited to those methods based on collecting engine data in a steady state and then performing fault detection. Alth...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06N3/088G06F18/217
Inventor 刘贞报李洋布树辉张超
Owner NORTHWESTERN POLYTECHNICAL UNIV
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