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Fault diagnosis method of pumping unit based on adaptive unscented kalman filter and rbf neural network

A neural network and fault diagnosis technology, applied in biological neural network models, computer components, special data processing applications, etc., can solve problems such as failure to detect pumping unit failures in time, missed maintenance periods, etc.

Active Publication Date: 2021-09-14
大庆瑞福佳石油科技有限公司
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Problems solved by technology

[0003] The present application provides a pumping unit fault diagnosis method based on adaptive unscented Kalman filter and RBF neural network to solve the problems caused by failure to detect the pumping unit failure in the prior art when the pumping unit is in operation. Technical problems that miss the best maintenance period

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  • Fault diagnosis method of pumping unit based on adaptive unscented kalman filter and rbf neural network
  • Fault diagnosis method of pumping unit based on adaptive unscented kalman filter and rbf neural network
  • Fault diagnosis method of pumping unit based on adaptive unscented kalman filter and rbf neural network

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[0065] The embodiment of the present application provides a method for fault diagnosis of pumping units based on adaptive unscented Kalman filter and RBF neural network, referring to the existing technical means, the technical solution provided by the present application has the following technical effects or advantages: the method The intelligent algorithm is used for pumping unit fault diagnosis, which effectively improves the diagnosis efficiency and truly achieves the purpose of pumping unit fault diagnosis.

[0066] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0067] Such as figure 1 As shown, a pumping unit fault diagnosis method based on adaptive unscented Kalman filter and RBF neural network includes the following steps:

[0068] Such as figure 2 , 3 , 4, 5, and 6, S1: When selecting a group of decision v...

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Abstract

The invention provides a pumping unit fault diagnosis method based on adaptive unscented Kalman filter and RBF neural network. First, use the RBF neural network to model the decision parameters, and then use the unscented Kalman Kalman filter algorithm to update the weight, center and width of the hidden layer of the neural network model in real time to obtain the optimal parameters of the neural network. Finally, use the adaptive The filter algorithm is used to improve the stability of the model, and a pumping unit fault diagnosis method based on the combination of adaptive unscented Kalman filter and RBF neural network is established. The remarkable effect of this method is: the unscented Kalman filter has real-time update performance, so as to realize the nonlinear dynamic modeling of the RBF neural network, and the adaptive filter algorithm can improve the stability of the model and meet the requirements of model accuracy in complex environments. This method improves the accuracy of fault diagnosis, and truly achieves the purpose of real-time detection of the operation status of the pumping unit.

Description

technical field [0001] The invention relates to a pumping unit fault diagnosis technology, in particular to a pumping unit fault diagnosis method based on an adaptive unscented Kalman filter and an RBF neural network. Background technique [0002] The fault diagnosis of pumping units requires scientific and reasonable methods. At present, people mainly judge artificially based on the dynamometer diagram, and can only make qualitative analysis. The diagnosis results are affected by expert experience, technology, etc., and the diagnosis has a certain lag , can not achieve real-time accurate diagnosis. The operation process of the pumping unit has the characteristics of nonlinearity and strong coupling, which brings great difficulties to the fault diagnosis. The RBF neural network has a strong nonlinear mapping ability, which is suitable for solving nonlinear system modeling problems, and provides a new idea for the process modeling of the scheme. The present invention adopts...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/20G06N3/02G06K9/00E21B47/008
CPCG06N3/02G06F30/20E21B47/008G06F2218/02
Inventor 周伟李晓亮刘华超甘丽群易军李太福梁晓东辜小花
Owner 大庆瑞福佳石油科技有限公司