Gaussian Sampling Approach to deal with Imbalanced Telemetry Datasets in Industrial Applications

被引：0

作者：

Galve, Sergio ^{[1
]}

Puig, Vicenc ^{[2
]}

Vilajosana, Xavi ^{[1
]}

机构：

[1] Univ Oberta Catalunya, Wireless Networks Res Lab, Castelldefels 08860, Barcelona, Spain

[2] UPC, CSIC, Inst Robot & Informat Ind, Barcelona, Spain

来源：

2023 31ST MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION, MED | 2023年

关键词：

D O I：

10.1109/MED59994.2023.10185829

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Practical implementation of data analytics in industrial environments has always been a problematic area because of data availability and quality. In this paper, a Gaussian sampling methodology is proposed to address the problem of imbalanced telemetry datasets that is one of the root causes that make modelling less reliable. By generating subsets that achieve homogeneous density distributions this problem is addressed. By comparing the impact of this method with the baseline case of random sampling, this paper aims to address this problem and propose a practical solution. A case study based on an industrial cooling device is used to assess and illustrate the proposed approach.

引用

页码：605 / 611

页数：7

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