Equipment fault classification method based on dynamic weight combination of integrated increment

Abstract

The invention discloses an equipment fault classification method based on dynamic weight combination of integrated increment, and relates to an equipment fault diagnosis technology. The method is mainly divided into the following parts: performing unbalanced data processing, performing wavelet denoising and reconstruction, performing feature extraction through empirical mode decomposition, building a long-short-term memory neural network, building a support vector machine model, using a support vector machine for dynamically adjusting the weight of each classifier in the long-short-term memoryneural network and support vector machine combined model, and using an integrated incremental model for realizing rapid dynamic incremental learning. Based on the research, the dynamic weight combination classification model based on the integration increment is finally provided and applied to rolling bearing equipment fault diagnosis, and the classification accuracy of equipment fault diagnosisis improved.

Description

technical field [0001] The invention relates to equipment fault diagnosis technology, in particular to an equipment fault classification method based on dynamic weight combination of integrated increment. Background technique [0002] In recent years, the rapid development of information technology and industrial Internet of Things has promoted revolutionary innovations and breakthroughs in the manufacturing industry. Among them, intelligent manufacturing technology, as a sustainable manufacturing model, optimizes the design and manufacturing process of products with the help of computer modeling and simulation and the huge potential of information and communication technology. With the development of the Industrial Internet of Things and information technology, large-scale machinery and equipment in the manufacturing industry continue to generate massive operating data during the production process. Through the analysis and extraction of equipment fault information quickly ...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a device fault classification method based on integrated incremental dynamic weight combination, which aims to solve the problem of category imbalance in data samples by using over-sampling and under-sampling fusion technology, and use wavelet packets to remove vibration signal data. Noise processing, and then use the ESMD model to extract the characteristic parameters from the vibration signal, use the characteristic vector to train the combined model of the long-term short-term memory neural network and the support vector machine, and use the support vector machine to dynamically adjust the corresponding weights of each classifier in the combined classification model ; When there is new sample data, use the integrated incremental learning method to add the classification function of the new sample data while retaining the original classification function

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