Fault diagnosis method for bearing equipment

Abstract

The invention discloses a fault diagnosis method for bearing equipment, and relates to the technical field of fault diagnosis of bearing equipment. The method comprises the following steps: firstly, unbalanced data samples are processed by using a resampling method; and then feature mode extraction is carried out on newly-added data by using a deep learning algorithm, then the newly-added modes are input into an original integrated model for classification, a dynamic forgetting weight algorithm, an effective increment screening algorithm and an increment information fusion algorithm are further provided, and the weight of an increment merging mode is dynamically adjusted according to a calculated value. Finally, supervised classification is carried out on the weighted modes by using an incremental integrated SVM, so that real-time extraction of bearing equipment state features and reliable classification of fault modes are realized.

Description

technical field [0001] The invention relates to the technical field of bearing equipment fault diagnosis, in particular to a bearing equipment fault diagnosis method based on incremental fusion and dynamic weight integrated learning. Background technique [0002] Bearing equipment is mostly used in important fields such as industry, aviation, and national defense, and its failure consequences are relatively serious. Therefore, how to extract fault feature information from the operating state data of bearing equipment and conduct effective analysis, so as to complete fault diagnosis and prediction is becoming increasingly important, and has become an intelligent manufacturing industry. research hotspots in the field. Especially in the context of Industry 4.0, with the development of industrial Internet of Things and information technology, a large amount of operating status data emerges in the production process, making it possible to use big data analysis methods for fault d...

AI Technical Summary

Problems solved by technology

For example, the influence of noise data is not considered in the process of resampling, resulting in a serious decrease in classification accuracy

Although some methods take into account the influence of sample distribution information on the data processing process, they ignore the changes in the sample distribution state with the generation of massive new data.

These problems hinder the further application of the above-mentioned unbalanced processing method in the field of equipment fault diagnosis

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