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A Method of Engine Fault Prediction

A fault prediction and engine technology, applied in the field of aerospace vehicles, can solve problems such as difficulty in finding engine fault prediction methods, different signal characteristics, etc., to achieve the effects of easy operation, improved reliability, and overcoming nonlinearity

Active Publication Date: 2018-12-14
SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Since different engines and different operating conditions produce different signal characteristics, it is difficult to find a general engine fault prediction method. Some fault prediction methods currently proposed have certain limited application ranges.

Method used

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  • A Method of Engine Fault Prediction
  • A Method of Engine Fault Prediction

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Embodiment Construction

[0026] The present invention will be further described below with reference to the accompanying drawings and in combination with preferred embodiments.

[0027] Such as figure 1 As shown, the preferred embodiment of the present invention discloses a failure prediction method of an engine, specifically for the failure prediction of a gas turbine engine, comprising the following steps:

[0028] S101: Using 21 sensors to collect raw engine data;

[0029] 21 sensors are arranged on the gas turbine engine, and the measured physical quantities are: the total temperature of the fan inlet, the total temperature of the low-pressure compressor (LPC) outlet, the total temperature of the high-pressure compressor (HPC) outlet, and the total temperature of the low-pressure turbine (LPT). ) air outlet total temperature, fan air inlet pressure, bypass duct total temperature, high pressure compressor (HPC) air outlet total pressure, physical fan speed, physical core speed, engine pressure rat...

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Abstract

The invention discloses an engine fault prediction method. The method comprises the following steps that effective measurement data of a plurality of sensors of an engine are obtained; the effective measurement data are subjected to normalization processing; the effective measurement data obtained after normalization processing are sliced, and a plurality of slice samples are formed; the multiple slice samples are sent into a deep nerve network to be subjected to feature extraction, and after feature extraction, the slice samples are connected to a fully-connected network; and according to output of the fully-connected network, classification is conducted through a softmax classifier, and the left service life of the engine is determined. According to the provided engine fault prediction method, the process is small, operation is easy, and the engine fault prediction method can be widely applied to various engines of aircraft.

Description

technical field [0001] The invention relates to the field of aerospace vehicles, in particular to an engine failure prediction method. Background technique [0002] The engine is the core component in an aircraft, but it is also one of the most prone to failure. Once the aircraft fails, it will adversely affect the entire mechanical equipment. Therefore, it is very necessary to dig out an accurate and efficient engine failure prediction method to ensure the normal operation of the aircraft. [0003] At present, there are many methods for engine fault prediction, which can be mainly divided into model-based methods, data-based methods, etc. The effects of these methods largely depend on the preprocessing ability and feature extraction ability of the collected real data. Because different engines and different operating conditions produce different signal characteristics, it is difficult to find a general engine fault prediction method, and some fault prediction methods prop...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): F02C9/00G01M15/14
CPCF02C9/00F05D2220/32F05D2260/80F05D2260/821G01M15/14
Inventor 梁斌杨春春李志恒杨君芦维宁王学谦谭俊波
Owner SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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