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Fault detection method based on distributed nonlinear dynamic relation model

A nonlinear dynamic, relational model technology, applied in general control systems, control/regulation systems, comprehensive factory control, etc., can solve problems such as neural network overfitting

Active Publication Date: 2018-11-27
NINGBO UNIV
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Problems solved by technology

The most common neural network models generally include BP neural network and RBF neural network. BP neural network can fit any problem with arbitrary precision through error backpropagation, but BP neural network is prone to overfitting

Method used

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  • Fault detection method based on distributed nonlinear dynamic relation model
  • Fault detection method based on distributed nonlinear dynamic relation model
  • Fault detection method based on distributed nonlinear dynamic relation model

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Embodiment Construction

[0058] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and specific examples of implementation.

[0059] like figure 1 As shown, the invention discloses a fault detection method based on a distributed nonlinear dynamic relationship model. The specific implementation process of the method of the present invention and its superiority over existing methods will be described below in conjunction with an example of a specific industrial process.

[0060] The application object is from Tennessee-Eastman (TE) chemical process experiment, and the prototype is an actual process flow of Eastman chemical production workshop. Currently, the TE process has been widely used in fault detection research as a standard experimental platform due to its complexity. The whole TE process includes 22 measured variables, 12 manipulated variables, and 19 component measured variables. The TE process object can simulate a variety of ...

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Abstract

The invention discloses a fault detection method based on a distributed nonlinear dynamic relation model, and aims to establish distributed nonlinear dynamic relation models for respective measured variables, and to implement fault detection based on the distributed models. The main core of the method comprises establishing nonlinear dynamic relation models for respective measured variables by using a RBF neural network, and taking account of the self-correlation of the measured variables embodied at different sampling moments and the cross-correlation of the measured variables and other variables embodied at different sampling moment. Compared with a traditional method, the method of the invention, by using the RBF neural network algorithm, constructs nonlinear dynamic relation models embodied in different sampling moments for respective measured variables, and reflects the advantages and characteristics of distributed modeling. Secondly, the method of the present invention takes an error as an object to be monitored, and is very helpful for the subsequent establishment of a fault detection model by using a principal component analysis algorithm. Therefore, the method of the present invention is more suitable for the fault detection of dynamic processes.

Description

technical field [0001] The invention relates to a data-driven fault detection method, in particular to a fault detection method based on a distributed nonlinear dynamic relationship model. Background technique [0002] Under the trend of industrial big data, the degree of utilization of industrial big data reflects the high level of industrial management. As an important part of the entire production automation, the fault detection system occupies a pivotal position. Its goal is to timely alert the fault state in the production process. The technical means of realization have changed from the implementation method based on the mechanism model to the data-driven strategy. Due to the development of advanced instrument technology, the sampling time interval has been greatly shortened. The time series autocorrelation between sampling data is a problem that must be considered in the data-driven process monitoring method. The correlation between measurement variables is not only r...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418
CPCG05B19/41885G05B2219/32339Y02P90/02
Inventor 宋励嘉童楚东俞海珍
Owner NINGBO UNIV
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