Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

被引：50

作者：

Kudelina, Karolina ^{[1
]}

Vaimann, Toomas ^{[1
]}

Asad, Bilal ^{[1
]}

Rassolkin, Anton ^{[1
]}

Kallaste, Ants ^{[1
]}

Demidova, Galina ^{[1
]}

机构：

[1] Tallinn Univ Technol, Dept Elect Power Engn & Mechatron, EE-19086 Tallinn, Estonia

来源：

APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 06期

关键词：

fault diagnostics; machine learning; artificial intelligence; pattern recognition; neural networks; BEARING FAULT-DIAGNOSIS; LOGISTIC-REGRESSION; INDUCTION-MOTOR; CLASSIFICATION; ALGORITHM; NETWORK; FRAMEWORK; SIGNALS;

D O I：

10.3390/app11062761

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes.

引用

页数：19

共 99 条

[1] Fault Detection and Classification Based on Co-training of Semisupervised Machine Learning [J].