Self-adaptive learning system of numerical control machine fault diagnosis system in multi-agent structure

A fault diagnosis system and self-adaptive learning technology, applied in general control systems, control/regulation systems, program control, etc., to achieve the effects of strong regulation, fast speed, and easy understanding

Inactive Publication Date: 2013-06-12
中国科学院沈阳计算技术研究所有限公司
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Problems solved by technology

But they all have a common shortcoming, which is the dependence on expert experience. Variables such as network weights, hidden parameters, and vector thresholds are all defined by expert experience.

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  • Self-adaptive learning system of numerical control machine fault diagnosis system in multi-agent structure
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  • Self-adaptive learning system of numerical control machine fault diagnosis system in multi-agent structure

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Embodiment Construction

[0039] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0040] According to the characteristics of knowledge model in CNC machine tool fault diagnosis system based on multi-agent technology, an immune network structure model is proposed;

[0041] Generate the antibodies in the immune network according to the immune regulation algorithm of antigen similarity, and get the memory antibody set N ab ;

[0042] According to the inverse relationship between immune action and distance, the comprehensive immune response ability of each antibody in the network is obtained;

[0043] The artificial immune network proposed in the present invention is designed with the immune mechanism of the biological immune system. The biological immune system responds and extracts features to the external invading antigens through antibody cells. After the antibody cells are cloned, they form mutations that have high sensitiv...

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Abstract

The invention provides a self-adaptive learning system based on an artificial immunization network, provides a structural model and an immune adjustment algorithm of the artificial immunization network, and belongs to the field of numerical control system fault diagnosis, wherein the self-adaptive learning system based on the artificial immunization network is designed for a numerical control machine fault diagnosis system based on multi-agent technology. The structural model of the artificial immunization network is confirmed; antibodies in the immunization network is generated according to the immune adjustment algorithm of antigenic similarity, and a memory antibody set (Nab) is obtained; and according to the inverse ratio property between immunization and distance, comprehensive immune response ability of each antibody in the network is obtained. The self-adaptive learning system suitable for resolving of complex problems has the advantages of avoiding local minimum, getting rid of limitation of expert experience and being high in algorithm speed and accuracy.

Description

technical field [0001] The invention designs an adaptive learning mechanism based on an artificial immune network for a fault diagnosis system of a numerically controlled machine tool based on multi-agent technology, proposes a structural model of the immune network and an immune regulation algorithm, and relates to the field of fault diagnosis of numerical control systems. Background technique [0002] Fault diagnosis has always been one of the core units of CNC system design, and it is also the functional module that best reflects its intelligence. Numerical control system is a large-scale complex fuzzy knowledge system, and its fault diagnosis has the characteristics of large amount of knowledge, complex relationship between various subsystems, and various methods of knowledge acquisition and knowledge expression. The traditional expert system construction method is very weak in fuzzy knowledge reasoning, diagnostic knowledge learning, etc., and can no longer meet the dev...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/406
Inventor 郭锐锋张函耿聪王峰杨磊陈龙
Owner 中国科学院沈阳计算技术研究所有限公司
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