Fan fault detecting method based on depth belief network model

A deep belief network and fault detection technology, applied in wind turbines, engines, mechanical equipment, etc., can solve problems such as long computing time, easy to fall into local optimum, and complex neural network training methods.

Active Publication Date: 2018-02-23
JIANGSU FRONTIER ELECTRIC TECH +1
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AI Technical Summary

Problems solved by technology

Neural network and support vector machine are two relatively mature algorithms, but the traditional neural network training method is complex and easy to fall into local optimum
Support v

Method used

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  • Fan fault detecting method based on depth belief network model
  • Fan fault detecting method based on depth belief network model
  • Fan fault detecting method based on depth belief network model

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Experimental program
Comparison scheme
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Embodiment

[0115] Select the data for fan status detection. To select the flow rate as the prediction vector, it is necessary to determine the fan parameters related to the fan flow rate as the modeling variables in the observation vector. The Pearson correlation coefficient is used to measure the variable correlation. The Pearson correlation coefficient of each fan status parameter and air volume signal is shown in Table 1.

[0116] Table 1

[0117] parameter name

Pearson correlation coefficient

Fan outlet pressure

0.9592

Fan current (hardwired)

0.8399

Motor coil temperature

0.7351

Motor drive end bearing temperature

0.6467

Motor free end bearing temperature

0.6243

Fan outlet air temperature

0.6169

fan vibration

0.5493

Bearing temperature

0.4827

Fan inlet air temperature

0.4430

Fan oil supply temperature

0.1989

Fan oil tank temperature

0.1004

[0118] Accordin...

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Abstract

The invention discloses a fan fault detecting method based on a depth belief network model. The fan fault detecting method comprises that a fan flow forecast model is established by using depth beliefnetwork, state parameters related to the fan flow are selected to be used as the input variable of the model, and the flow of a fan is forecasted. The depth belief network is formed by staking multilayer continuous type limit Boltzmann machines, and an adaptive step length method is used to accelerate the algorithm training process. In addition, residual distribution characteristics are calculated according to a sliding window, when the mean value or standard deviation of the residual exceeds the threshold, alarm is issued. By means of the fan fault detecting method based on the depth beliefnetwork model, the flow of the fan can be accurately forecasted, meanwhile, the abnormal working state of the fan can be detected, and fault detection on the fan is achieved.

Description

technical field [0001] The invention belongs to the field of thermal automatic control, and relates to a fan fault detection method, in particular to a fan fault detection method based on a deep belief network model. Background technique [0002] The fan is very important to ensure the safe and reliable operation of the entire power generation system, and it is the equipment that needs to be monitored during the operation of the unit. [0003] With the demand for peak-shaving capacity of the power grid and the increasing installed capacity of generators, wind turbines are often in the operating state of high parameters, large vibration, poor working conditions, and fast load adjustment. The reliability of wind turbine operation decreases and the failure rate increases. In addition, the structure of the fan is complex and the system is highly nonlinear, so it is difficult to establish an accurate analytical mathematical model of the equipment. [0004] With the development o...

Claims

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Application Information

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IPC IPC(8): F03D17/00
CPCF03D17/00F05B2260/84
Inventor 孙栓柱刘旭婷张友卫王林周春蕾李益国王明许国强杨晨琛周志兴魏威佘国金肖明成
Owner JIANGSU FRONTIER ELECTRIC TECH
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