Equipment state prediction method and system based on multi-dimensional data fusion

Abstract

The invention discloses an equipment state prediction method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting and preprocessing a state monitoring signal of an equipment operation full life cycle, and carrying out noise reduction of the state monitoring signal through wavelet packet analysis; performing time domain, frequency domain and time-frequency domain feature extraction on the original state monitoring signal and the intrinsic mode component, performing feature screening by using permutation entropy and information entropy, performing unsupervised identification of equipment working conditions on the screened features, performing model training at a cloud center end; and storing to an edge end to predict the running state and the residual life of the equipment. Useful multi-dimensional data information in similar working conditions is mined by using a multi-task learning method so as to improve the regression performance of the equipment state and residual life prediction model, and a cloud-side combined system architecture is adopted to save the communication overhead and improve the calculation efficiency.

Description

technical field [0001] The present invention relates to the technical field of equipment state prediction, in particular to an equipment state prediction method and system based on multi-dimensional data fusion. Background technique [0002] The safe and reliable operation of industrial field equipment is not only the prerequisite for ensuring the stable improvement of the economic and social benefits of enterprises, but also the stable basis for ensuring the safety of operators. Therefore, predictive maintenance of industrial equipment has become an indispensable part of industrial production, and Equipment operation status information and life information are the main objects of equipment maintenance. Accurately predicting the future status of equipment and its failure time can reduce the defective rate of corresponding production workpieces, improve the turnover efficiency of the entire industrial process, and then improve production efficiency. . However, if excessive p...

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Problems solved by technology

[0007] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to effectively excavate useful sensitive features in a large number of equipment operation status monitoring signals to represent the real-time status of equipment operation and its remaining life information; Effectively identify working conditions of heterogeneous equipment and judge similar working conditions and dissimilar working conditions to achieve sample aggregation with similar working conditions; use useful information in similar working conditions to build equipment operating state prediction models to provide more accurate real-time status information and failure time prediction

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