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Research on Multi-Physical Domain Information Fusion Method of Intelligent Processing Machine Based on GMM-HMM

机译:基于GMM-HMM的智能加工机多物理域信息融合方法研究

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As a typical unit of intelligent manufacturing system, intelligent processing machine is also a microcosm of Intelligent Manufacturing System (IMS), and the realization of intelligent manufacturing system will be firstly embodied in the intelligent processing machine as the core of intelligence. Therefore, it is inevitable to do a comprehensive and systematic study of intelligent processing machines and will get more and more attentions. In the study of intelligent machines, perceptual technology is an important part. Without effective perceptual technology, intelligent machines cannot interact with the environment, and thus the intelligence level cannot be improved. At present, the quite popular approach is to study the connection between perception and behavior from various simple behaviors and realize the higher intelligence through the combination of these behaviors on the basis of simple behaviors. The basis of autonomy perception of intelligent machines is to make it have the capability of multi-physical domain information fusion of manufacturing process and environment. An improved data processing and information fusion method based on GMM-HMM is proposed, which provides a self-perception of intelligent processing machines and a multi-physical domain information fusion method [1]. The simulation results show that the method proposed in this paper can realize the self - identification of intelligent processing machines in the intelligent manufacturing environment, such as the state of manufacturing, health and fault.
机译:作为智能制造系统的典型单位,智能加工机也是智能制造系统(IMS)的微观形式,并将智能制造系统的实现首先体现在智能化加工机中作为智能核心。因此,这是对智能加工机器的全面和系统的研究是不可避免的,并将越来越多地注意。在智能机器的研究中,感知技术是一个重要的部分。如果没有有效的感知技术,智能机器不能与环境进行交互,因此无法改善智能水平。目前,相当流行的方法是研究来自各种简单行为的感知和行为之间的联系,并在简单行为的基础上通过这些行为的组合实现更高的智能。自治机器的自主感知的基础是使其具有制造过程和环境的多物理域信息融合的能力。提出了一种基于GMM-HMM的改进的数据处理和信息融合方法,它提供了智能处理机器的自我感知和多物理域信息融合方法[1]。仿真结果表明,本文提出的方法可以实现智能制造环境中智能加工机器的自识,如制造,健康和故障。

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