Fault Diagnosis of an Electrohydraulic System by Using Fuzzy C-Means Clustering

Hakan Güner; Seniz Ertugrul; Gökhan Tansel Tayyar

doi:10.1007/978-3-031-67195-1_35

Fault Diagnosis of an Electrohydraulic System by Using Fuzzy C-Means Clustering

Hakan Güner^*, Seniz Ertugrul, Gökhan Tansel Tayyar

^*Corresponding author for this work

Department of Naval Architecture and Marine Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Hydraulic systems typically operate under harsh conditions, such as in the heavy industry and military domain. Early diagnosis of single or multiple faults is very important to keep the system in safe working conditions. To generate different faults, an exhaustive simulation stage is required before validating the study with an experimental setup. Therefore, in this study, an electrohydraulic system model was first created using the Matlab-Simscape hydraulic system library. After the simulation stage, a data-based fault diagnosis method was applied through certain statistical feature calculations using time, frequency, and time-based frequency of the data collected using the Diagnostic Feature Designer Toolbox under the Matlab program, which allows the use of all signal and data-based debugging, identification, and classification methods under the same platform. Based on the features ranked by the Diagnostic Feature Designer Toolbox, the best fault model fits were investigated by clustering with Fuzzy C-Means.

Original language	English
Title of host publication	Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A. Cagri Tolga
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	293-303
Number of pages	11
ISBN (Print)	9783031671944
DOIs	https://doi.org/10.1007/978-3-031-67195-1_35
Publication status	Published - 2024
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2024 - Canakkale, Turkey Duration: 16 Jul 2024 → 18 Jul 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1089 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2024
Country/Territory	Turkey
City	Canakkale
Period	16/07/24 → 18/07/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Clustering
Fault Diagnosis
Feature Design
Fuzzy C-Means

Access to Document

10.1007/978-3-031-67195-1_35

Cite this

Güner, H., Ertugrul, S., & Tayyar, G. T. (2024). Fault Diagnosis of an Electrohydraulic System by Using Fuzzy C-Means Clustering. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. Ucal Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference (pp. 293-303). (Lecture Notes in Networks and Systems; Vol. 1089 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-67195-1_35

Güner, Hakan ; Ertugrul, Seniz ; Tayyar, Gökhan Tansel. / Fault Diagnosis of an Electrohydraulic System by Using Fuzzy C-Means Clustering. Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A. Cagri Tolga. Springer Science and Business Media Deutschland GmbH, 2024. pp. 293-303 (Lecture Notes in Networks and Systems).

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abstract = "Hydraulic systems typically operate under harsh conditions, such as in the heavy industry and military domain. Early diagnosis of single or multiple faults is very important to keep the system in safe working conditions. To generate different faults, an exhaustive simulation stage is required before validating the study with an experimental setup. Therefore, in this study, an electrohydraulic system model was first created using the Matlab-Simscape hydraulic system library. After the simulation stage, a data-based fault diagnosis method was applied through certain statistical feature calculations using time, frequency, and time-based frequency of the data collected using the Diagnostic Feature Designer Toolbox under the Matlab program, which allows the use of all signal and data-based debugging, identification, and classification methods under the same platform. Based on the features ranked by the Diagnostic Feature Designer Toolbox, the best fault model fits were investigated by clustering with Fuzzy C-Means.",

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author = "Hakan G{\"u}ner and Seniz Ertugrul and Tayyar, {G{\"o}khan Tansel}",

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doi = "10.1007/978-3-031-67195-1_35",

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Güner, H, Ertugrul, S & Tayyar, GT 2024, Fault Diagnosis of an Electrohydraulic System by Using Fuzzy C-Means Clustering. in C Kahraman, S Cevik Onar, B Oztaysi, I Ucal Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. Lecture Notes in Networks and Systems, vol. 1089 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 293-303, International Conference on Intelligent and Fuzzy Systems, INFUS 2024, Canakkale, Turkey, 16/07/24. https://doi.org/10.1007/978-3-031-67195-1_35

Fault Diagnosis of an Electrohydraulic System by Using Fuzzy C-Means Clustering. / Güner, Hakan; Ertugrul, Seniz; Tayyar, Gökhan Tansel.
Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A. Cagri Tolga. Springer Science and Business Media Deutschland GmbH, 2024. p. 293-303 (Lecture Notes in Networks and Systems; Vol. 1089 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Fault Diagnosis of an Electrohydraulic System by Using Fuzzy C-Means Clustering

AU - Güner, Hakan

AU - Ertugrul, Seniz

AU - Tayyar, Gökhan Tansel

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Hydraulic systems typically operate under harsh conditions, such as in the heavy industry and military domain. Early diagnosis of single or multiple faults is very important to keep the system in safe working conditions. To generate different faults, an exhaustive simulation stage is required before validating the study with an experimental setup. Therefore, in this study, an electrohydraulic system model was first created using the Matlab-Simscape hydraulic system library. After the simulation stage, a data-based fault diagnosis method was applied through certain statistical feature calculations using time, frequency, and time-based frequency of the data collected using the Diagnostic Feature Designer Toolbox under the Matlab program, which allows the use of all signal and data-based debugging, identification, and classification methods under the same platform. Based on the features ranked by the Diagnostic Feature Designer Toolbox, the best fault model fits were investigated by clustering with Fuzzy C-Means.

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Güner H, Ertugrul S, Tayyar GT. Fault Diagnosis of an Electrohydraulic System by Using Fuzzy C-Means Clustering. In Kahraman C, Cevik Onar S, Oztaysi B, Ucal Sari I, Cebi S, Tolga AC, editors, Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. Springer Science and Business Media Deutschland GmbH. 2024. p. 293-303. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-67195-1_35

Fault Diagnosis of an Electrohydraulic System by Using Fuzzy C-Means Clustering

Abstract

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Keywords

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