Electromechanical equipment-oriented remote dynamic adaptive rule acquisition method

Abstract

The invention relates to an electromechanical equipment-oriented remote dynamic adaptive rule acquisition method. The electromechanical equipment-oriented remote dynamic adaptive rule acquisition method comprises the steps as follows: (1) acquiring state signals of function parts of electromechanical equipment by a sensor group; (2) uploading the state signals into a remote data system through a remote network, converting and storing the state signals in a unified data format; (3) transmitting the data of the state signals to a fault diagnosis module of a remote network platform, obtaining intrinsic mode functions representing the characteristics of the state signals by a variable scale empirical mode decomposition method, and carrying out Hilbert conversion on the intrinsic mode functions respectively so as to obtain corresponding instantaneous frequencies; and (4) constructing a remote fault diagnosis knowledge base system, obtaining a rule base composed of rules, and transmitting to a tool fault diagnosis and prediction service platform through the remote network; and (5) carrying out dynamic adaptive optimization updating on the obtained rule base according to a dynamic coordination method by the tool fault diagnosis and prediction service platform, and taking degree of confidence as evaluation of the rules.

Description

technical field [0001] The invention relates to a method for acquiring a remote dynamic self-adaptive rule base, in particular to a method for acquiring a remote dynamic self-adaptive rule for electromechanical equipment. Background technique [0002] Mechanical and electrical equipment such as CNC machine tools with the theme of large-scale, high-speed, precision, and multi-combination have become the main processing equipment in my country's manufacturing industry. With the rapid development of the manufacturing industry, the mechanical structure of CNC equipment has become more and more complex, and the degree of integration, precision and intelligence has become higher and higher. This increases the possibility of accuracy degradation and mechanical failures during the processing of the equipment. At the same time, due to the uncertainty of the processing parameters and working conditions of the machine tool during the processing process and the influence of the surround...

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Problems solved by technology

At the same time, due to the lack of unified description and storage of these uncertain data, the remote monitoring and diagnosis system cannot sort out and apply them, thus failing to better support the optimal operation of the machine tool processing system

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