Non-linear process industrial fault prediction and identification method based on compressed sensing and DROS-ELM

A compressed sensing and fault prediction technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as loss of life and property, and achieve the effect of improving production efficiency, increasing economic benefits, and having fewer adjustable parameters

Active Publication Date: 2015-04-22
BEIJING UNIV OF CHEM TECH
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Problems solved by technology

[0002] At present, with the complexity of large-scale industrial system processes and the increasing number of control links and c...

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  • Non-linear process industrial fault prediction and identification method based on compressed sensing and DROS-ELM
  • Non-linear process industrial fault prediction and identification method based on compressed sensing and DROS-ELM
  • Non-linear process industrial fault prediction and identification method based on compressed sensing and DROS-ELM

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[0026] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0027] The invention provides a high-performance nonlinear process industrial fault prediction and identification method, which overcomes the difficulty of online fault prediction and identification in the nonlinear production process of complex industrial systems, applies compressed sensing and artificial neural networks to the industrial field, and constructs The fault prediction and recognition model based on compressed sensing feature extraction and dynamic feedback OS-ELM neural network (DROS-ELM) technology provides technical support for enterprises to ensure safe production, improve production efficiency and save production costs.

[0028] like figure 1 Shown is th...

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Abstract

The invention relates to a non-linear process industrial fault prediction and identification method based on compressed sensing and dynamic recurrent online sequential-extreme learning machine (DROS-ELM). According to the high-performance non-linear process industrial fault prediction and identification method, a problem of shortage of the on-line fault prediction and identification during the non-linear production process of the complicated industrial system can be solved. The compressed sensing and the artificial neutral network are applied to the industrial field and thus fault prediction and identification models based on the compressed sensing feature extraction and dynamic feedback OS-ELM neutral network technology is respectively constructed, thereby realizing fault prediction. Therefore, a technical support can be provided for guaranteed safety production, improved production efficiency, and saved production cost of the enterprise.

Description

technical field [0001] The invention relates to the field of industrial control, in particular to a nonlinear process industrial fault prediction and identification method based on compressed sensing and DROS-ELM. Background technique [0002] At present, with the complexity of large-scale industrial system processes and the increasing number of control links and control points, once a safety accident occurs in many major and difficult-to-observe hazards, it will cause huge loss of life and property. [0003] In recent years, accidents caused by system equipment failures have occurred frequently, and fault prediction and identification technology has also attracted the attention of scholars at home and abroad. It has become an urgent need to realize system-wide fault prediction and identification. The fault prediction and identification method needs to judge whether a fault occurs in the system in the future according to the past and current state of the system, and accurate...

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Application Information

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IPC IPC(8): G06N3/02G06N3/08G06F19/00
Inventor 徐圆叶亮亮朱群雄耿志强周子茜米川黄兵明刘莹卢玉帅申生奇
Owner BEIJING UNIV OF CHEM TECH
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