Pumping unit fault diagnosis method based on improved unscented Kalman filter and RBF neural network

A neural network and fault diagnosis technology, applied in biological neural network models, computer components, pattern recognition in signals, etc., can solve problems such as failing to detect pumping unit faults in time, missing the best maintenance period, etc., to improve efficiency Effect

Active Publication Date: 2018-11-13
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0003] The present application provides a pumping unit fault diagnosis method based on improved unscented Kalman filtering and RBF neural network to solve the problems caused by the inability to detect the pumping unit failure in the prior art when a fault occurs during the operation of the pumping unit. Technical Issues in Optimum Maintenance Period

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  • Pumping unit fault diagnosis method based on improved unscented Kalman filter and RBF neural network
  • Pumping unit fault diagnosis method based on improved unscented Kalman filter and RBF neural network
  • Pumping unit fault diagnosis method based on improved unscented Kalman filter and RBF neural network

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[0059] The embodiments of the present application provide a fault diagnosis method for pumping units based on the improved unscented Kalman filter and RBF neural network, referring to the existing technical means, the technical solution provided by the application has the technical effects or advantages: the method adopts The intelligent algorithm is used in the pumping unit fault diagnosis, which effectively improves the diagnosis efficiency and truly achieves the purpose of pumping unit fault diagnosis.

[0060] 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.

[0061] like figure 1 As shown, a pumping unit fault diagnosis method based on improved unscented Kalman filter and RBF neural network includes the following steps:

[0062] like figure 2 , 3 , 4, 5, and 6, S1: When selecting a group of decision variables: sel...

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Abstract

The invention provides a pumping unit fault diagnosis method based on an improved unscented Kalman filter and an RBF neural network. Firstly, modeling is performed on decision parameters by using theRBF neural network; then, the weights, center and width of the hidden layer of a neural network model are updated by using an mproved unscented Kalman filter algorithm in real time to obtain optimal parameters of the neural network; and the pumping unit fault diagnosis method based on the combination of the improved unscented Kalman filter and the RBF neural network is established. The remarkableeffect of the invention lies in that: the accuracy rate of fault diagnosis is improved, and the purpose of real-time detection of the operation condition of a pumping unit is truly achieved.

Description

technical field [0001] The invention relates to a fault diagnosis technology for a pumping unit, in particular to a fault diagnosis method for a pumping unit based on an improved unscented Kalman filter and a 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 inven...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06N3/02G06K9/00E21B47/008
CPCG06N3/02G06F30/20E21B47/008G06F2218/02
Inventor 李晓亮周伟甘丽群刘华超易军李太福梁晓东辜小花
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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