Machine error data classification method and system

Abstract

The invention relates to the technical field of data mining, in particular to a machine error data classification method and system. A label propagation algorithm is introduced as a machine error data preprocessing step, and labels of uncalibrated data are rapidly estimated by the aid of a small number of calibrated machine data to form a classification training set. Based on the machine data and the labels in the classification training set, dictionary learning with consistent labels is performed, reconstruction errors are minimized, sparse coding errors are judged, the errors are classified, a reconstruction dictionary, sparse codes and a multi-class linear classifier are obtained, and the relationship among items in the dictionary and the data labels is kept. Furthermore, machine data characteristics are represented by the obtained sparse codes and inputted to the classifier for prediction, classes of test samples are determined, and the machine data errors are classified. By introducing efficient semi-supervised data preprocessing, the number of calibrated machine samples is increased, prior information is enriched, and machine data classification accuracy is effectively improved.

Description

technical field [0001] The present application relates to the technical field of data mining, in particular to a method and system for classifying machine error data. Background technique [0002] With the continuous development of computer technology and intelligence, machine error data classification has become a very important research topic in data mining. Among them, the machine error data classification technology describes the machine error data, analyzes the data structure, and then obtains the data characteristics, and finally classifies the machine error data according to the data characteristics. [0003] In the prior art, dictionary learning algorithms, such as K-SVD and D-KSVD (Discriminative K-SVD), are usually used to learn the machine data in the training set and the calibration of the machine data to obtain a reconstructed dictionary, sparse coding And the classifier, and use the obtained sparse coding to represent the characteristics of the machine data, a...

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Problems solved by technology

[0004] In view of this, the present application provides a machine error data classification method and system to solve the problem that when the number of samples in the selected training set is small in the prior art, it is easy to cause the characteristics of the machine error data to be accurately represented, and to reduce the machine error data. The problem of classification accuracy

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