Data driven incremental integration based screw type fault diagnosis model

Abstract

The invention discloses a data driven incremental integration based screw type fault diagnosis model. A method comprises the following steps that data points are collected and divided into normal samples and fault samples; random sampling is conducted, and unbalance samples of different slope rates are obtained and divided into four groups; relative balance samples are obtained through a resampling method based on dividing neighbors; the relative balance samples are input into DAE to extract fault features, when new data exists, feature patterns are incrementally combined, and then the samplesare input into SVM for fault diagnosis; cases which have information content and are rich in representativeness are selected, and dynamic comprehensive evaluation is conducted on effective features and the cases; an effective case set and the new data are combined, and the incremental learning process is conducted again. The model obtains balance data beneficial to accurate fault type identification on the condition that sample noise and distribution features are fully considered, by conducting dynamic evaluation and incremental combination through selection features and the cases, effectiveinformation is reserved and passed on, and then rapid and efficient incremental learning and classification diagnosis of equipment faults are achieved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of bearing equipment, in particular to a spiral fault diagnosis model based on data-driven incremental fusion. Background technique [0002] Smart devices are mostly used in important fields such as industry, aviation, and national defense, and the consequences of their failures are relatively serious. In recent years, intelligent manufacturing, as the core content of Industry 4.0, has gradually developed into an important research field. At the same time, with the development of the Industrial Internet of Things, a large amount of operating data continues to emerge in the production process of large-scale equipment. Quickly and efficiently analyzing and extracting fault information through operating data, and effectively diagnosing and predicting fault types, has become a research field in the field of intelligent manufacturing. hotspot. [0003] With the deep integration of information...

AI Technical Summary

Problems solved by technology

Compared with the traditional incremental learning method, this model makes the fault data reach a relative balance by adding the proposed resampling technology based on the division of neighbors, and selects and transfers both informative and representative instances to maximize the retention of all valid information. And the dynamic forgetting weight is used to comprehensively evaluate the extracted features and selected examples, forming a new spiral fault diagnosis model dynamically connected with incremental information, which effectively solves the problem of massive, unbalanced, high noise, and unstable equipment fault data. , strong causal correlation and other characteristics, realize the unbalanced incremental learning of fault data and the dynamic evaluation and transmission of effective features and instances, and achieve the effect of accurate and efficient identification of incremental fault information in equipment operation data

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