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A dynamic process monitoring method based on bp neural network autoregressive model

A technology of BP neural network and autoregressive model, which is applied in biological neural network models, neural learning methods, neural architectures, etc., and can solve problems such as which data cannot be sampled into

Active Publication Date: 2020-10-27
镇江云游信息科技有限公司
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Problems solved by technology

The training data for process monitoring is usually a single-category model, and it is generally impossible to simply divide the sampled data into which ones are used as inputs and which ones are used as outputs, because any abnormal changes in measured variables are an external manifestation of failures.

Method used

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  • A dynamic process monitoring method based on bp neural network autoregressive model
  • A dynamic process monitoring method based on bp neural network autoregressive model
  • A dynamic process monitoring method based on bp neural network autoregressive model

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Embodiment Construction

[0049] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and specific examples of implementation.

[0050] Such as figure 1 As shown, the present invention discloses a dynamic process monitoring method based on BP neural network autoregressive model. The specific implementation process of the method of the present invention will be described below in conjunction with an example of a specific industrial process.

[0051] Before introducing specific implementation cases, it is necessary to briefly introduce the basic principles of the BP neural network. Let the input mode of BP neural network be α=[α 1 , α 2 ,...,α dm ], the output of the hidden layer is β=[β 1 , β 2 ,...,β 2dm ], the output of the output layer is γ=[γ 1 , gamma 2 ,…, γ m ], the actual output value of the neural network is y=[y 1 ,y 2 ,...,y m ]. According to the principle of BP neural network, the hidden layer output β can be obtain...

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Abstract

The invention discloses a dynamic process monitoring method based on a BP neural network auto-regression model, aims at establishing a nonlinear auto-regression model by using a BP neural network andimplement dynamic process monitoring on this basis. The main core of the method of the invention firstly lies in that the BP neural network is utilized to identify an autocorrelation model of sampleddata of a monitored object, and secondly lies in that an error after filtering by the BP neural network auto-regression model is utilized to implement process monitoring. The method provided by the invention has the main advantages that firstly, a nonlinear auto-regression model is established by utilizing a nonlinear fitting capability of the BP neural network, thereby achieving the purpose of removing nonlinear autocorrelation characteristics in measurement variables; and secondly, the method provided by the invention utilizes a capability of the error of reflecting the abnormal change condition of the nonlinear autocorrelation characteristics, and since autocorrelation no longer exists in error data, convenience is provided for subsequent establishment of a fault detection model. Thus,the method provided by the invention is more suitable for dynamic process monitoring.

Description

technical field [0001] The invention relates to a data-driven process monitoring method, in particular to a dynamic process monitoring method based on a BP neural network autoregressive model. Background technique [0002] Modern process industry processes are usually in a continuous and efficient production state, and the operational requirements for ensuring product quality stability, production safety, and operating state stability are increasingly expected for the reliability and effectiveness of the process monitoring system. high. Under the trend of industrial big data, the technical means of implementing process monitoring using mechanism models are becoming less and less suitable for the monitoring requirements of modern process industry processes. In addition, the degree of utilization of industrial big data reflects the high level of industrial management. Therefore, data-driven process monitoring methods are favored in this context. Due to the development of ad...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B19/418G06N3/04G06N3/08
CPCG05B19/41885G06N3/049G06N3/08Y02P90/02
Inventor 宋励嘉童楚东俞海珍
Owner 镇江云游信息科技有限公司
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