An Evolving Fuzzy Classifier for Induction Motor Health Condition Monitoring

Peter Luong,Wilson Wang

doi:10.4236/ica.2019.104009

Abstract

Induction motor (IM) is commonly used in various industrial applications. Reliable online IM health condition monitoring systems are critically needed in industries to improve operational accuracy and safety of the IMs and the machinery. A new evolving algorithm is proposed to provide more decision-making transparency, as well as better classification and processing efficiency. The effectiveness of the developed intelligent classifier is examined by simulation and experimental tests.

Highlights

Induction motors (IMs) are widely used in industrial applications such as pumping stations, manufacturing facilities, electric vehicles, etc
An EF classifier has been developed in this work for IM health condition monitoring
Its effectiveness is verified by simulation tests using some benchmark datasets

Summary

Introduction

Induction motors (IMs) are widely used in industrial applications such as pumping stations, manufacturing facilities, electric vehicles, etc. IMs consume about 40% of the electrical power generated in the world [2]; there is a strong incentive to ensure that IMs operate efficiently and do not break down unexpectedly To achieve this goal, research has been conducted over decades to develop technologies and tools to detect IM faults at their incipient stage, prior to reaching more serious levels, so as to prevent performance degradation, malfunction, or even catastrophic failures of the IMs and the related driven facilities. Research has been conducted over decades to develop technologies and tools to detect IM faults at their incipient stage, prior to reaching more serious levels, so as to prevent performance degradation, malfunction, or even catastrophic failures of the IMs and the related driven facilities This active monitoring process is known as condition monitoring [3], which serves as a form of predictive maintenance strategy.

Methods

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Discussion

Conclusion