Enhancing Fault Detection in Distributed Motor Systems Using AI-Driven Cyber-Physical Sensor Networks

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Title

Enhancing Fault Detection in Distributed Motor Systems Using AI-Driven Cyber-Physical Sensor Networks

Content partner

Auckland University of Technology

Collection

Tuwhera

Description

Defect detection in distributed motors within the IoED architecture is the focus of this research. The idea of the distributed Internet of Things (DIoT) is used to build a cyber-physical system architecture. To improve sensitivity and accuracy, this approach uses fast Fourier transform (FFT) for signal processing and an ANN for defect detection. When it comes to motor conditions, ANNs can adapt to different situations and find complicated patterns, whereas FFT is good at extracting frequency ...

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Format

Research paper

Research format

Conference item

Date created

2024-11-26

Creator

Altaf, Saud / Al-Anbuky, Adnan / Gheitasi, Alireza

URL

https://openrepository.aut.ac.nz/handle/10292/18623

Related subjects

4605 Data Management and Data Science / 4606 Distributed Computing and Systems Software / 46 Information and Computing Sciences / 40 Engineering

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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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