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 language | English |
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Pages (from-to) | 511-518 |
Number of pages | 8 |
Journal | Procedia Manufacturing |
Volume | 43 |
DOIs | |
State | Published - 2020 |
Event | 17th Global Conference on Sustainable Manufacturing 2019 - Shanghai, China Duration: Oct 9 2019 → Oct 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