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Aero-engine sensor intelligent analysis redundancy design method based on KEOS-ELM algorithm

A technology of aero-engine and redundancy design, applied in design optimization/simulation, instrumentation, calculation, etc.

Active Publication Date: 2019-04-16
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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However, this integrated algorithm does not evaluate the learning effect of a single network, but uniformly averages multiple models as the final output, which may lead to the weight and other effects of some networks with poor learning effect good network same

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  • Aero-engine sensor intelligent analysis redundancy design method based on KEOS-ELM algorithm
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  • Aero-engine sensor intelligent analysis redundancy design method based on KEOS-ELM algorithm

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Embodiment Construction

[0074] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0075] A kind of intelligent analytical redundancy design method of aeroengine sensor based on KEOS-ELM algorithm of the present invention, such as figure 1 As shown, it specifically includes the following steps:

[0076] Step 1), establish the four sensor signal prediction models based on the KEOS-ELM algorithm of aeroengine high-pressure speed NH, compressor outlet temperature T3, compressor outlet pressure P3, and turbine outlet pressure P5;

[0077] Step 1.1), normalize the four sensor signals of the aero-engine high-pressure speed NH, compressor outlet temperature T3, compressor outlet pressure P3, and turbine outlet pressure P5;

[0078] Step 1.2), for each sensor signal, establish a signal prediction model based on KEOS-ELM, the specific expression is:

[0079] NH forecasting model:

[0080]

[0081] T3 prediction model:

[008...

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Abstract

The invention discloses an aero-engine sensor intelligent analysis redundancy design method based on a KEOS-ELM algorithm. The method comprises the following steps of: establishing Four sensor signalprediction models of the aero-engine high-pressure rotating speed NH, the compressor outlet temperature T3, the compressor outlet pressure P3 and the turbine outlet pressure P5 based on a KEOS-ELM algorithm; adopting a Kalman filtering algorithm and an online learning strategy to update KEOS-ELM network topology parameters; performing sensor fault signal online identification according to the predicted residual error and the residual error change rate; and performing sensor fault isolation and signal reconstruction based on the KEOS-ELM algorithm. According to the method, the problems of untimely fault identification, misdiagnosis, missed diagnosis and the like of an existing sensor drift fault diagnosis method based on prediction residual errors are solved, sensor bias and drift faults can be identified in time, and the method plays a positive role in promoting real-time health management of an aero-engine and reducing the maintenance cost.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis and signal reconstruction of aero-engine sensors, and in particular relates to an intelligent analysis margin design method for aero-engine sensors based on the KEOS-ELM algorithm. Background technique [0002] Aeroengines work under harsh conditions of high temperature, high pressure, and high load all year round, and their working status changes frequently, which leads to frequent engine sensor failures. However, the aero-engine closed-loop control system is inseparable from sensor measurement signals. The sensor is the main device to obtain the working data of the engine. When the performance of the sensor deteriorates, malfunctions or fails, it will have a serious impact on the subsequent monitoring, control and fault diagnosis systems. Sensor fault diagnosis technology is to analyze the fault information of the sensor in real time and give an alarm in time through various data proces...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/15G06F30/20Y02T90/00
Inventor 鲁峰吴金栋黄金泉吴斌仇小杰
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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