Learning features from vibration signals for induction motor fault diagnosis

Siyu Shao, Wenjun Sun, Peng Wang, Robert X. Gao, Ruqiang Yan

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

37 Scopus citations

Abstract

Aiming at automated and intelligent state monitoring of induction motors, which are an integral component of a broad spectrum of manufacturing machines, this paper presents a Deep Belief Network (DBN)-based approach to automatically extract relevant features from vibration signals that characterize the working condition of an induction motor. The DBN model employs a structure with stacked restricted Boltzmann machines (RBMs), and is trained by an efficient learning algorithm called greedy layer-wise training. Vibration signals are used as the input to the DBN, and the outputs from activation functions of the trained network are the features needed for fault diagnosis. Comparing to traditional feature extraction methods for induction motor fault diagnosis such as wavelet packet transform, the proposed method is able to learn features directly from the vibration signal to achieve comparable performance with high classification accuracy. Experiments conducted on a machine fault simulator have verified the effectiveness of the proposed method for induction motor fault diagnosis.

Original languageEnglish
Title of host publicationInternational Symposium on Flexible Automation, ISFA 2016
Pages71-76
Number of pages6
ISBN (Electronic)9781509034673
DOIs
StatePublished - Dec 16 2016
EventInternational Symposium on Flexible Automation, ISFA 2016 - Cleveland, United States
Duration: Aug 1 2016Aug 3 2016

Publication series

NameInternational Symposium on Flexible Automation, ISFA 2016

Conference

ConferenceInternational Symposium on Flexible Automation, ISFA 2016
Country/TerritoryUnited States
CityCleveland
Period8/1/168/3/16

Bibliographical note

Funding Information:
This work has been supported in part by the National Natural Science Foundation of China under 51575102 and the National Science Foundation of US under CCF-1331850 and CMMI-1300999.

Publisher Copyright:
© 2016 IEEE.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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