Document Type

Article

Publication Date

1-28-2020

Publication Source

Journal Of The Electrochemical Society

Abstract

The downtime of industrial machines, engines, or heavy equipment can lead to a direct loss of revenue. Accurate prediction of such failures using sensor data can prevent or reduce the downtime. With the availability of Internet of Things (IoT) technologies, it is possible to acquire the sensor data in real-time. Machine Learning and Deep Learning (DL) algorithms can then be used to predict the part and equipment failures, given enough historical data. DL algorithms have shown significant advances in problems where progress has eluded the practitioners and researchers for several decades. This paper reviews the DL algorithms used for predictive maintenance and presents a case study of engine failure prediction. We also discuss the current use of sensors in the industry and future opportunities for electrochemical sensors in predictive maintenance.

© 2020 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 License (CC BY, http://creativecommons.org/licenses/ by/4.0/), which permits unrestricted reuse of the work in any medium, provided the original work is properly cited. [DOI: 10.1149/ 1945-7111/ab67a8]

ISBN/ISSN

0013-4651

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1149/1945-7111/ab67a8

Publisher

Electrochemical Soc Inc

Volume

167

Issue

3

Peer Reviewed

yes

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