Original scientific paper

https://doi.org/10.21278/TOF.471040722

The Matthew Effect of a Fault Classification Mechanism and Its Application

Shihua Wang ; School of Science, Guangdong University of Petrochemical TechnologyMaoming, China
Shaolin Hu ; School of Automation, Guangdong University of Petrochemical Technology, Maoming, China
Qinghua Zhang ; School of Automation, Guangdong University of Petrochemical Technology, Maoming, China

Abstract

When using the classification algorithm to classify a single sample, the classification accuracy often cannot achieve an ideal effect. To solve this problem, the following two aspects of research work are carried out and presented in this paper. On the one hand, according to the memory characteristics of mechanical faults, a voting classification mechanism for the sample sequence to be classified is proposed. It is found that the classification mechanism of the sample sequence to be classified with memory has the Matthew effect of accumulated advantage. Using this effect, one can improve the accuracy of fault classification. On the other hand, because the length of the sample sequence to be classified increases, the delay of the classification results increases. To solve this problem, the classification algorithm is optimized to minimize the delay on the assumption that the classification accuracy meets the expected requirements.

Keywords

fault diagnosis; machine learning; the Matthew effect

Hrčak ID:

299475

URI

https://hrcak.srce.hr/299475

Publication date:

19.4.2023.

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