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Data-driven Key Features Selection for Fault Detection in a Complex System

Published: 01 February 2021 Publication History

Editorial Notes

The editors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on March 5, 2021. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.

Abstract

Fault detection is a difficult but important problem for a complex system. This paper presents a fault detection method based on data-driven key feature selection for the complex system abbreviated as FD-DKFS. By regarding the observable parameters as original features, FD-DKFS first finds the missing correlations among original features and constructs potentially useful features for fault detection. Next, FD-DKFS provides a filter feature selection method to find the best feature subset for fault detection. Then, these selected features are used to detect the fault in a certain complex system with conventional classifiers. Compared with the other methods, the results of the experiment show that the proposed method is more accurate for fault detection in the complex system.

Supplemental Material

PDF File - 3443842-vor
Version of Record for "Data-driven Key Features Selection for Fault Detection in a Complex System" by Zhou et al., Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering (EITCE '20).

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  1. Data-driven Key Features Selection for Fault Detection in a Complex System

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    cover image ACM Other conferences
    EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering
    November 2020
    1202 pages
    ISBN:9781450387811
    DOI:10.1145/3443467
    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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    Published: 01 February 2021

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    Author Tags

    1. Fault detection
    2. complex system
    3. feature construction
    4. feature selection

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    EITCE '20 Paper Acceptance Rate 214 of 441 submissions, 49%;
    Overall Acceptance Rate 508 of 972 submissions, 52%

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