A Review of Fault Diagnosis for Traction Induction Motor

  • Yin Tian
  • , Dingfei Guo
  • , Kunting Zhang
  • , Lihao Jia
  • , Hong Qiao
  • , Haichuan Tang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

With the rapid development of traction motor, the mechanical health monitoring and fault diagnosis field have entered the era of big data. Definite harmonic signals of the line current are located by a popular method known as motor current signature analysis. Different faults of an induction motor such as rotor, stator, bearing, vibration, air gap eccentricity and their different diagnosis techniques are also explored. In fact, the actual fault detection also has a deep development in the artificial intelligence. It is truly evident that the scope of this area is vast. Hence, acknowledging the need for future research, this review paper presents a birds eye view on different types of traction induction faults and their diagnostics schemes.

Original languageEnglish
Title of host publicationProceedings of the 37th Chinese Control Conference, CCC 2018
EditorsXin Chen, Qianchuan Zhao
PublisherIEEE Computer Society
Pages5763-5768
Number of pages6
ISBN (Electronic)9789881563941
DOIs
StatePublished - 5 Oct 2018
Event37th Chinese Control Conference, CCC 2018 - Wuhan, China
Duration: 25 Jul 201827 Jul 2018

Publication series

NameChinese Control Conference, CCC
Volume2018-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference37th Chinese Control Conference, CCC 2018
Country/TerritoryChina
CityWuhan
Period25/07/1827/07/18

Keywords

  • Artificial intelligence
  • Current analysis
  • Fault diagnosis
  • Model-based approach
  • Traction induction motors

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