Article

Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea

Authors:

Lakshman Krishnamurthy,

Phil Buonadonna,

Jasmeet Chhabra,

Nandakishore Kushalnagar,

Mark YarvisAuthors Info & Claims

SenSys '05: Proceedings of the 3rd international conference on Embedded networked sensor systems

Pages 64 - 75

https://doi.org/10.1145/1098918.1098926

Published: 02 November 2005 Publication History

Abstract

Sensing technology is a cornerstone for many industrial applications. Manufacturing plants and engineering facilities, such as shipboard engine rooms, require sensors to ensure product quality and efficient and safe operation. We focus on one representative application, preventative equipment maintenance, in which vibration signatures are gathered to predict equipment failure. Based on application requirements and site surveys, we develop a general architecture for this class of industrial applications. This architecture meets the application's data fidelity needs through careful state preservation and over-sampling. We describe the impact of implementing the architecture on two sensing platforms with differing processor and communication capabilities. We present a systematic performance comparison between these platforms in the context of the application. We also describe our experience and lessons learned in two settings: in a semiconductor fabrication plant and onboard an oil tanker in the North Sea. Finally, we establish design guidelines for an ideal platform and architecture for industrial applications. This paper includes several unique contributions: a study of the impact of platform on architecture, a comparison of two deployments in the same application class, and a demonstration of application return on investment.

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Cited By

Irfan HLiao PTaipabu MWu W(2024)Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln ProcessSensors10.3390/s2405148624:5(1486)Online publication date: 24-Feb-2024
https://doi.org/10.3390/s24051486
Riccio CMenanno MZennaro ISavino M(2024)A New Methodological Framework for Optimizing Predictive Maintenance Using Machine Learning Combined with Product Quality ParametersMachines10.3390/machines1207044312:7(443)Online publication date: 27-Jun-2024
https://doi.org/10.3390/machines12070443
Liang QKnutsen KVanem EÆsøy VZhang H(2024)A review of maritime equipment prognostics health management from a classification society perspectiveOcean Engineering10.1016/j.oceaneng.2024.117619301(117619)Online publication date: Jun-2024
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Index Terms

Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems
2. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

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Published In

cover image ACM Conferences

SenSys '05: Proceedings of the 3rd international conference on Embedded networked sensor systems

November 2005

340 pages

ISBN:159593054X

DOI:10.1145/1098918

General Chair:
Jason Redi
BBN Technologies, USA
,
Program Chairs:
Hari Balakrishnan
University of Massachusetts, USA
,
Feng Zhao
Microsoft Research, USA

Copyright © 2005 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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Publication History

Published: 02 November 2005

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SenSys05

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SenSys05: ACM Conference on Embedded Network Sensor Systems

November 2 - 4, 2005

California, San Diego, USA

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Overall Acceptance Rate 174 of 867 submissions, 20%

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Irfan HLiao PTaipabu MWu W(2024)Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln ProcessSensors10.3390/s2405148624:5(1486)Online publication date: 24-Feb-2024
https://doi.org/10.3390/s24051486
Riccio CMenanno MZennaro ISavino M(2024)A New Methodological Framework for Optimizing Predictive Maintenance Using Machine Learning Combined with Product Quality ParametersMachines10.3390/machines1207044312:7(443)Online publication date: 27-Jun-2024
https://doi.org/10.3390/machines12070443
Liang QKnutsen KVanem EÆsøy VZhang H(2024)A review of maritime equipment prognostics health management from a classification society perspectiveOcean Engineering10.1016/j.oceaneng.2024.117619301(117619)Online publication date: Jun-2024
https://doi.org/10.1016/j.oceaneng.2024.117619
Madadi MRezaei SKhojandi A(2024)Dynamic joint sensor selection and maintenance optimization in partially observable deteriorating systemsComputers & Industrial Engineering10.1016/j.cie.2023.109853187(109853)Online publication date: Jan-2024
https://doi.org/10.1016/j.cie.2023.109853
Bhushan SShanker SCengiz K(2023)The Industrial Internet of ThingsHandbook of Research on Data-Driven Mathematical Modeling in Smart Cities10.4018/978-1-6684-6408-3.ch021(396-414)Online publication date: 17-Feb-2023
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Pullwitt SSchlichter JWolf L(2023)UAV-assisted Multiband WSN Coverage Evaluation2023 19th International Conference on the Design of Reliable Communication Networks (DRCN)10.1109/DRCN57075.2023.10108189(1-6)Online publication date: 17-Apr-2023
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Jovicic EPrimorac DCupic MJovic A(2023)Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A ReviewIEEE Access10.1109/ACCESS.2023.329511311(73505-73520)Online publication date: 2023
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Nguyen TMohammadi F(2023)Cyber-Physical Power and Energy Systems with Wireless Sensor Networks: A Systematic ReviewJournal of Electrical Engineering & Technology10.1007/s42835-023-01482-318:6(4353-4365)Online publication date: 3-Apr-2023
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Schlichter JVogt MAgrawal NWolf LHerrmann C(2022)Enabling Energy Efficient HVAC Operation Through IWSNsIEEE Transactions on Green Communications and Networking10.1109/TGCN.2021.31053706:1(132-147)Online publication date: Mar-2022
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Schlichter JWolf L(2022)Design and Deployment Experiences of a Versatile Industrial WSN and Testbed2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)10.1109/DCOSS54816.2022.00043(1-8)Online publication date: May-2022
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