Home // UBICOMM 2020, The Fourteenth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies // View article

Fault Diagnosis for Industrial Rotary Machinery based on Edge Computing and Neural Networking

Authors:
Valentin Perminov
Vladislav Ermakov
Dmitry Korzun

Keywords: Fault diagnosis; convolutional neural network; edge computing; vibration diagnostics

Abstract:
Recent progress in Sensorics and Internet of Things (IoT) enables real-time data analytics based on data from multiple sensors covering the target industrial production system and its manufacturing processes. Diagnostics and prognosis can be implemented using the neural network approach on top of vibration and other sensed data. Neural network methods lead to high accuracy in fault detection and fault evolution. Nevertheless, transferring a neural network model to edge devices leads to performance issues and platform limitations. In this paper, we discuss the edge computing opportunities for diagnostics of industrial rotary machinery using well-known neural network methods.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2020

Publication date: October 25, 2020

Published in: conference

ISSN: 2308-4278

ISBN: 978-1-61208-811-2

Location: Nice, France

Dates: from October 25, 2020 to October 29, 2020