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

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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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 research did not realize the diagnosis of the vibration fault of the wind turbine generator nacelle. method, or just adopt a relatively simple shallow statistical and analysis method, the diagnosis method for the vibration fault of the wind turbine nacelle is not enough to meet the existing productivity

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