Fan cabin vibration fault diagnosis system and method based on naive Bayesian algorithm

A technology of fault diagnosis system and Bayesian algorithm, which is applied to computer parts, calculations, instruments, etc., can solve the problem that the vibration fault diagnosis method of the wind turbine nacelle is not realized, and the vibration fault diagnosis method of the wind turbine nacelle is not enough to meet the productivity and other issues to achieve the effect of ensuring the economic benefits of production, reducing operation and maintenance costs, and stabilizing the operating state

Pending Publication Date: 2022-02-25
国电电力宁夏新能源开发有限公司
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Problems solved by technology

[0004] These two literatures respectively mentioned the method of diagnosing the vibration fault of the turbine generator based on the Bayesian classification algorithm, and the method of analyzing the vibration spectrum signal to judge the fault of the wind turbine generator, but the previous resea

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  • Fan cabin vibration fault diagnosis system and method based on naive Bayesian algorithm
  • Fan cabin vibration fault diagnosis system and method based on naive Bayesian algorithm
  • Fan cabin vibration fault diagnosis system and method based on naive Bayesian algorithm

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[0046] Embodiment:

[0047] Such as Figure 1-3 As shown, the embodiment of the present invention provides a fan cabin vibration fault diagnosis system based on a simplicibian algorithm, including the vibration failure of the cabin vibration failure in the underlying Hive database;

[0048] Calculating the data stored by the API call, the computing server analyzes the data, generates the corresponding calculation result, and saves in the database;

[0049] The web server used to make a business display, the web server will calculate the results via the visual design, perform the corresponding business display, integrated in a centralized monitoring control platform.

[0050]The calculation server includes a data acquisition module, a data pre-processing module, a data split module, a model build module, a model test module, and a model evaluation module. The data acquisition module is connected to the data pre-processing module, and the data pre-processing module is connected to th...

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Abstract

The invention provides a fan cabin vibration fault diagnosis system and method based on a naive Bayes algorithm, and relates to the technical field of fault diagnosis. According to the fan cabin vibration fault diagnosis system based on the naive Bayesian algorithm, cabin vibration fault related historical operation data collected by a bottom layer are taken from a big data HIVE database; a calculation server for storing the data through api is called to analyze and calculate the data, generate a corresponding calculation result, and store the calculation result in the database; and a Web server is used for carrying out service display, and the Web server carries out corresponding service display on a calculation result through visual design and integrates the calculation result in a centralized monitoring control platform. According to the method, the degradation trend of the engine room can be effectively checked before the vibration fault of the fan engine room in combination with business cognition and evaluation scores, the engine room is regulated and controlled in advance, the fault frequency of the engine room is reduced, the operation and maintenance cost of the engine room is reduced, the operation state is stabilized, and the production economic benefits are guaranteed.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a vibration fault diagnosis system and method for a fan cabin based on a naive Bayesian algorithm. Background technique [0002] The working conditions of wind turbines are complex and changeable, and the failure rate is high. It is difficult to achieve effective vibration fault diagnosis with a single signal processing method. Through the analysis and processing of the vibration of the unit and related signals, it can be judged whether the unit is in normal operation, and the faults can be diagnosed. Diagnose the cause, point out the cause of the failure, apply the expert knowledge system, make maintenance suggestions and maintenance time suggestions before bad events occur, and reduce the failure rate of the unit. [0003] In 2010, Gao Junshan et al. proposed a vibration detection method for turbogenerators based on rough set theory and naive Bayesian classification alg...

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Application Information

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IPC IPC(8): G06K9/62G06F16/2458G06Q10/00G06Q50/06
CPCG06F16/2462G06Q10/20G06Q50/06G06F18/24155
Inventor 李刚邹学李骥渠叶君刘俊燕孙亚飞
Owner 国电电力宁夏新能源开发有限公司
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