Aero-engine gas circuit part fault detection method

A technology for aero-engine and gas path components, which is used in engine testing, machine/structural component testing, measuring devices, etc. It can solve problems such as increasing the computational burden and complexity of implementation, and achieve the effect of realizing the fault-tolerant function of the sensor.

Active Publication Date: 2016-03-02
上海航数智能科技有限公司
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Problems solved by technology

However, the above method based on the centralized Kalman filter only uses the estimated information of a single filter, which is more complicated to implement and increases the computational burden

Method used

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  • Aero-engine gas circuit part fault detection method
  • Aero-engine gas circuit part fault detection method
  • Aero-engine gas circuit part fault detection method

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Embodiment Construction

[0042] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0043] Since the fused extended Kalman filter is estimated in the form of a filter bank, the redundant information between each sub-filter and the main filter can be used to detect sensor faults, and the estimation results of bad subsystems have great influence on the system fusion solution. The contribution of will be small, and faulty subsystems can be easily isolated, reducing the impact on the global estimate. Therefore, the extended Kalman filter with fusion structure can well complete the fusion diagnosis of aeroengine gas circuit component faults and sensor faults. The working section of the gas path of the turbofan engine involved in the embodiments of the present invention is as follows: figure 1 As shown, the main components include intake duct, fan, compressor, combustion chamber, high-pressure turbine, low-p...

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Abstract

The invention relates to an aero-engine gas circuit part fault detection method, and solves problems that the calculation burden is heavy and the fault tolerance is poor when a standard extended Kalman filtering algorithm employs a centralized-type structure for gas circuit health diagnosis. Moreover, the designed sensor fault detection and isolation method based on state consistency can achieve the fault tolerance function of the sensor. The method takes a filtering structure of a fusion structure as the basis, and a fault detection unit is additionally disposed between each local filter and a main filter. The sensor fault detection is carried out under the condition that when the sensor is normal, all local filters are consistent in state estimation. If a fault happens to a subsystem, the subsystem is isolated. When fusion is carried out in the main filter, the information of a fault subsystem is eliminated, and the method just carries out information fusion and feedback on a normal subsystem, thereby achieving the fault diagnosis of a gas circuit with a function of sensor fault tolerance.

Description

technical field [0001] The invention relates to a fault detection method of an aero-engine air circuit component, belonging to the technical field of aero-engine fault diagnosis. 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 air circuit components of aeroengines is mainly to estimate the changes of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M15/00
CPCG01M15/00
Inventor 王亚凡鲁峰黄金泉黄一桓
Owner 上海航数智能科技有限公司
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