Channel State Information (CSI) analysis for Predictive Maintenance using Convolutional Neural Network (CNN)

Prachi Bagave*, Jeroen Linssen, Wouter Teeuw, Jeroen Klein Brinke, Nirvana Meratnia

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    2 Citations (Scopus)

    Abstract

    With the onset of the fourth industrial revolution, predictive maintenance using wireless sensing technologies has been in high demand. This motivates to investigate the potential of WiFi CSI as a sensor for understanding the operation of machines. Since rotating motors are one of the fundamental elements in many complex machines, this paper focuses on the classification of CSI signals influenced by rotating motors at different speeds. As WiFi CSI technology is still not mature, we focus on data collection and study the sensitivity and reliability of data for this type of applications. We observe that CNNs are suitable to classify the speeds of motors and is also sensitive to speeds close to each other when operated in ideal network condition. However, in practical network conditions, unreliability of the data and the inability of CNN to classify it remains a challenge.
    Original languageEnglish
    Title of host publicationDATA'19
    Subtitle of host publicationProceedings of the 2nd Workshop on Data Acquisition To Analysis
    Place of PublicationNew York
    Pages51-56
    Number of pages6
    ISBN (Electronic)978-1-4503-6993-0
    DOIs
    Publication statusPublished - 2019
    Event2nd Workshop on Data Acquisition To Analysis, DATA 2019 - Columbus University, New York, United States
    Duration: 10 Nov 201910 Nov 2019
    Conference number: 2

    Conference

    Conference2nd Workshop on Data Acquisition To Analysis, DATA 2019
    Abbreviated titleDATA
    CountryUnited States
    CityNew York
    Period10/11/1910/11/19

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