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Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis

Publikation: Beiträge in Zeitschriften › Übersichtsarbeiten › Forschung

Standard

Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis. / Mercorelli, Paolo.
in: Sensors, Jahrgang 24, Nr. 8, 2656, 22.04.2024.

Publikation: Beiträge in Zeitschriften › Übersichtsarbeiten › Forschung

Harvard

Mercorelli, P 2024, 'Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis', Sensors, Jg. 24, Nr. 8, 2656. https://doi.org/10.3390/s24082656

APA

Mercorelli, P. (2024). Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis. Sensors, 24(8), Artikel 2656. https://doi.org/10.3390/s24082656

Vancouver

Mercorelli P. Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis. Sensors. 2024 Apr 22;24(8):2656. doi: 10.3390/s24082656

Bibtex

@article{a7c83eb86b964492b0fa7189e40fc668,
title = "Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis",
abstract = "Fault-finding diagnostics is a model-driven approach that identifies a system{\textquoteright}s malfunctioning portion. It uses residual generators to identify faults, and various methods like isolation techniques and structural analysis are used. However, diagnostic equipment doesn{\textquoteright}t measure the remaining signal-to-noise ratio. Residual selection identifies fault-detecting generators. Fault detective diagnostic (FDD) approaches have been investigated and implemented for various industrial processes. However, industrial operations make it difficult to implement FDD techniques. To bridge the gap between theoretical methodologies and implementations, hybrid approaches and intelligent procedures are needed. Future research should focus on improving fault prognosis, allowing for accurate prediction of process failures and avoiding safety hazards. Real-time and comprehensive FDD strategies should be implemented in the age of big data.",
keywords = "fault detection techniques, fault location techniques, high impedance fault, literature review, modeling, Engineering",
author = "Paolo Mercorelli",
note = "Publisher Copyright: {\textcopyright} 2024 by the author.",
year = "2024",
month = apr,
day = "22",
doi = "10.3390/s24082656",
language = "English",
volume = "24",
journal = "Sensors",
issn = "1424-8239",
publisher = "MDPI AG",
number = "8",

}

RIS

TY - JOUR

T1 - Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis

AU - Mercorelli, Paolo

N1 - Publisher Copyright: © 2024 by the author.

PY - 2024/4/22

Y1 - 2024/4/22

N2 - Fault-finding diagnostics is a model-driven approach that identifies a system’s malfunctioning portion. It uses residual generators to identify faults, and various methods like isolation techniques and structural analysis are used. However, diagnostic equipment doesn’t measure the remaining signal-to-noise ratio. Residual selection identifies fault-detecting generators. Fault detective diagnostic (FDD) approaches have been investigated and implemented for various industrial processes. However, industrial operations make it difficult to implement FDD techniques. To bridge the gap between theoretical methodologies and implementations, hybrid approaches and intelligent procedures are needed. Future research should focus on improving fault prognosis, allowing for accurate prediction of process failures and avoiding safety hazards. Real-time and comprehensive FDD strategies should be implemented in the age of big data.

AB - Fault-finding diagnostics is a model-driven approach that identifies a system’s malfunctioning portion. It uses residual generators to identify faults, and various methods like isolation techniques and structural analysis are used. However, diagnostic equipment doesn’t measure the remaining signal-to-noise ratio. Residual selection identifies fault-detecting generators. Fault detective diagnostic (FDD) approaches have been investigated and implemented for various industrial processes. However, industrial operations make it difficult to implement FDD techniques. To bridge the gap between theoretical methodologies and implementations, hybrid approaches and intelligent procedures are needed. Future research should focus on improving fault prognosis, allowing for accurate prediction of process failures and avoiding safety hazards. Real-time and comprehensive FDD strategies should be implemented in the age of big data.

KW - fault detection techniques

KW - fault location techniques

KW - high impedance fault

KW - literature review

KW - modeling

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85191374601&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/fdf4cf9f-fe46-3c4a-aaa1-619f9bac3a27/

U2 - 10.3390/s24082656

DO - 10.3390/s24082656

M3 - Scientific review articles

C2 - 38676272

AN - SCOPUS:85191374601

VL - 24

JO - Sensors

JF - Sensors

SN - 1424-8239

IS - 8

M1 - 2656

ER -

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