research-article

Log-based predictive maintenance

Authors:

Dmitriy Fradkin,

Fabian Moerchen,

Zhuang WangAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1867 - 1876

https://doi.org/10.1145/2623330.2623340

Published: 24 August 2014 Publication History

Abstract

Success of manufacturing companies largely depends on reliability of their products. Scheduled maintenance is widely used to ensure that equipment is operating correctly so as to avoid unexpected breakdowns. Such maintenance is often carried out separately for every component, based on its usage or simply on some fixed schedule. However, scheduled maintenance is labor-intensive and ineffective in identifying problems that develop between technician's visits. Unforeseen failures still frequently occur. In contrast, predictive maintenance techniques help determine the condition of in-service equipment in order to predict when and what repairs should be performed. The main goal of predictive maintenance is to enable pro-active scheduling of corrective work, and thus prevent unexpected equipment failures.

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Cited By

Shahra EWu WBasurra SAneiba A(2024)Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution SystemWater10.3390/w1602019616:2(196)Online publication date: 5-Jan-2024
https://doi.org/10.3390/w16020196
Souza PFerreto TCalheiros R(2024)Maintenance Operations on Cloud, Edge, and IoT Environments: Taxonomy, Survey, and Research ChallengesACM Computing Surveys10.1145/365909756:10(1-38)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3659097
Al-Anzi FAl-Anzi ASarath S(2024)Predictive maintenance in industrial IoT (IIoT)International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024)10.1117/12.3030667(9)Online publication date: 26-Jun-2024
https://doi.org/10.1117/12.3030667
Show More Cited By

Index Terms

Log-based predictive maintenance
1. Applied computing
  1. Operations research
    1. Industry and manufacturing
2. Social and professional topics
  1. Professional topics
    1. Computing and business
      1. Automation

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Published In

cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 24 August 2014

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KDD '14

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Shahra EWu WBasurra SAneiba A(2024)Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution SystemWater10.3390/w1602019616:2(196)Online publication date: 5-Jan-2024
https://doi.org/10.3390/w16020196
Souza PFerreto TCalheiros R(2024)Maintenance Operations on Cloud, Edge, and IoT Environments: Taxonomy, Survey, and Research ChallengesACM Computing Surveys10.1145/365909756:10(1-38)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3659097
Al-Anzi FAl-Anzi ASarath S(2024)Predictive maintenance in industrial IoT (IIoT)International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024)10.1117/12.3030667(9)Online publication date: 26-Jun-2024
https://doi.org/10.1117/12.3030667
Jing XWu ZZhang LLi ZMu D(2024)Electrical Fault Diagnosis From Text Data: A Supervised Sentence Embedding Combined With Imbalanced ClassificationIEEE Transactions on Industrial Electronics10.1109/TIE.2023.326946371:3(3064-3073)Online publication date: Mar-2024
https://doi.org/10.1109/TIE.2023.3269463
Bhanuse VGangurde NDube SDevi JDoifode H(2024)Predictive Maintenance of AC using Multiple Parameters2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)10.1109/IDCIoT59759.2024.10467353(326-330)Online publication date: 4-Jan-2024
https://doi.org/10.1109/IDCIoT59759.2024.10467353
Liu ZHui J(2024)Advancing predictive maintenance: a deep learning approach to sensor and event-log data fusionSensor Review10.1108/SR-03-2024-018344:5(563-574)Online publication date: 9-Jul-2024
https://doi.org/10.1108/SR-03-2024-0183
Gashi MMutlu BThalmann S(2023)Impact of Interdependencies: Multi-Component System Perspective toward Predictive Maintenance Based on Machine Learning and XAIApplied Sciences10.3390/app1305308813:5(3088)Online publication date: 27-Feb-2023
https://doi.org/10.3390/app13053088
Elhabbash ARogoda KElkhatib Y(2023)MARTIN: An End-to-end Microservice Architecture for Predictive Maintenance in Industry 4.02023 IEEE International Conference on Software Services Engineering (SSE)10.1109/SSE60056.2023.00013(10-19)Online publication date: Jul-2023
https://doi.org/10.1109/SSE60056.2023.00013
Bhaskar A(2023)Industrial IoT Condition Monitoring using Wireless IoT Sensor2023 International Conference on Artificial Intelligence and Smart Communication (AISC)10.1109/AISC56616.2023.10085613(831-837)Online publication date: 27-Jan-2023
https://doi.org/10.1109/AISC56616.2023.10085613
Bhardwaj AVeeramani DZhou S(2023)Confidently extracting hierarchical taxonomy information from unstructured maintenance records of industrial equipmentInternational Journal of Production Research10.1080/00207543.2023.216701361:23(8159-8178)Online publication date: 24-Jan-2023
https://doi.org/10.1080/00207543.2023.2167013
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