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Gas turbine fault diagnosis expert system method based on GA and L-M combined optimization

A fault diagnosis and gas turbine technology, applied in the direction of gas turbine devices, gas turbine engine testing, reasoning methods, etc., can solve problems such as prone to failures and production accidents, and achieve the effect of ensuring diversity and improving stability

Pending Publication Date: 2019-10-15
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

As the core component of the gas turbine unit, the gas turbine operates under high temperature and high pressure conditions for a long time, which is prone to failure and production accidents

Method used

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  • Gas turbine fault diagnosis expert system method based on GA and L-M combined optimization
  • Gas turbine fault diagnosis expert system method based on GA and L-M combined optimization
  • Gas turbine fault diagnosis expert system method based on GA and L-M combined optimization

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Embodiment 1

[0045] Fault diagnosis, in short, is to judge the current fault state of the system according to the characteristic parameters of the system. Therefore, the fault diagnosis space of the system can be divided into two parts, namely feature space and state space. The feature space is all possible values ​​of the measurable characteristic parameters of the diagnostic object, and the state space is all possible states of the system (including fault state and normal state). When the diagnosed object is state S, we can determine the feature y, that is, there is a mapping G relationship:

[0046] G:S→y

[0047] Similarly, when the characteristic value of the object to be diagnosed is known, its corresponding state can also be determined, that is, there is a mapping F relationship:

[0048] F: y→S.

[0049] Such asfigure 1 As shown, there is a mapping relationship between the state space and the feature space of the system, and the state space will correspond to the corresponding f...

Embodiment 2

[0098] The number of neurons in the input layer of the BP network is 5, the number of neurons in the hidden layer is 11, and the number of neurons in the output layer is 10. The network error is in the form of a sum of squares, and the target error is set to 10 -3 , the maximum training times is 10000. In the present invention, three algorithms are respectively adopted to train gas turbine gas circuit fault samples, these three algorithms are: GA optimized BP algorithm, L-M optimized BP algorithm and GA and L-M combined optimized BP algorithm. The five neurons in the input layer of the BP network are respectively expressed as: the variation of the rotational speed of the low-pressure compressor (δn 1 %), high pressure compressor speed variation (δn 2 %), change in compression ratio of high pressure compressor (δπ HC %), change in compression ratio of low-pressure compressor (δπ LC %) and fuel flow rate change (δwf%) these five variables. In order to make the diagnostic re...

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Abstract

The invention discloses a gas turbine fault diagnosis expert system method based on GA and L-M combined optimization. The method comprises the following steps: S1, determining a BP neural network topological structure and an initial weight threshold of a BP neural network; S2, determining an optimal initial weight threshold of the BP neural network; S3, calculating an error through an L-M algorithm by using the optimal initial weight threshold, and updating the weight threshold to obtain a gas turbine fault diagnosis simulation prediction result; and S4, establishing a gas turbine fault diagnosis expert system based on LabView software, establishing and maintaining a system knowledge base based on the MySQL database, performing fault diagnosis calculation through GA and L-M neural networkalgorithms, and reasoning a final result. The algorithm model provided by the invention has a better training speed, and can meet the real-time requirement of gas turbine fault diagnosis. The LabViewexpert system has a good human-computer interface, the defects of LabVIEW in the aspects of numerical value and matrix operation and database processing can be overcome by combining Matlab and MySQL databases, and the LabView expert system has certain practicability.

Description

technical field [0001] The invention relates to gas turbine fault diagnosis expert system technology, in particular to a gas turbine fault diagnosis expert system method based on combined optimization of GA and L-M. Background technique [0002] In recent years, under the guidance of various national energy-saving and emission-reduction policies, gas-fired power generation technology with outstanding advantages such as green and pollution-free, small size, and quick start-stop has been vigorously promoted, and the installed capacity of domestic gas turbines has continued to increase. As the core component of a gas turbine unit, the gas turbine operates under high-temperature and high-pressure conditions for a long time, and it is prone to malfunction and cause production accidents. Therefore, it is of great significance to implement gas turbine condition monitoring and diagnosis and establish a fault diagnosis expert system to ensure the safe and reliable operation of gas tu...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/08G06N5/04G01M15/14F02C9/00
CPCG06N3/084G06N3/086G06N5/04G01M15/14F02C9/00F05D2260/80G06F30/20
Inventor 温鑫钱玉良石宪王丹周硕
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER