Multi-stream convolutional neural network-based fault diagnosis for variable frequency drives in sustainable manufacturing systems

John Grezmak, Jianjing Zhang, Peng Wang, Robert X. Gao

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations

Abstract

Fault detection and diagnosis of induction motors in variable frequency drive (VFD) applications is essential for minimizing unexpected downtime, material waste and equipment damage, ultimately contributing to sustainable manufacturing. This paper presents a multi-stream convolutional neural network (MS-CNN) for automatic feature extraction from and fusion of motor vibration and stator current at various line frequencies. The MS-CNN has demonstrated superior performance over conventional machine learning methods. To understand the rationale for MS-CNN to diagnose motor defects, the relevance of input features for fault classification by a trained MS-CNN are investigated through Layer-wise Relevance Propagation (LRP) of its predictions.

Original languageEnglish
Pages (from-to)511-518
Number of pages8
JournalProcedia Manufacturing
Volume43
DOIs
StatePublished - 2020
Event17th Global Conference on Sustainable Manufacturing 2019 - Shanghai, China
Duration: Oct 9 2019Oct 11 2019

Bibliographical note

Publisher Copyright:
© 2020 The Authors. Published by Elsevier B.V.

Keywords

  • Fault diagnosis
  • convolutional neural network
  • variable frequency drive

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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