Robust machine error retrieving method and system

Abstract

The invention discloses a robust machine error retrieving method and a robust machine error retrieving system. Firstly, training set data is pre-processed by utilizing a tag estimation method, a tag of uncalibrated machine data is estimated, and a projection classifier is initialized. Based on class information of a training sample, tag consistence dictionary learning is carried out; self-adaptive reconstruction weight in a tag predication model is configured by obtained sparsity-judgment coding, and the class information of non-tag training data is updated by computing a novel projection classifier. One judged reconfigurable dictionary, one sparse coding matrix and one optimal multi-class classifier are output by multi-time iterative training. The classifier obtained by training can be used for concluding newcomer data and carrying out class predication on the newcomer data; and according to a position corresponding to a maximum probability value in a soft tag, the class of a tested sample is determined, so that robust classifying of machine error data is completed. A semi-supervised tag consistency dictionary learning method is disclosed, so that supervised prior information is enriched, and machine error retrieving precision is effectively improved.

Description

technical field [0001] The invention relates to the technical field of data mining and computer vision, and in particular, to a method and system for robust machine error retrieval. Background technique [0002] With the continuous development of computer technology and intelligence, machine error classification has developed into a very important research topic in data mining. Machine error classification technology digitizes machine data by computer, then analyzes the data structure and obtains data characteristics, which is of great significance in the field of mechanical fault diagnosis. Once the research is successful and put into application, it will produce huge social and economic benefits. [0003] Most of the current research work focuses on fully supervised or unsupervised methods for extracting machine data features for machine misclassification, and has also achieved certain results. But the machine data in the real world is usually a small amount of labels, an...

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Problems solved by technology

[0006] In view of this, the present invention provides a robust machine error retrieval method and system to overcome the problem of high overhead in obtaining data labels in the prior art

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