Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis
Condition monitoring and fault classification in engineering systems is a critical challenge within the scope of Prognostics and Health Management (PHM). The fault diagnosis of complex nonlinear systems, such as hydraulic systems, has become increasingly important due to advancements in big data ana...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024172829 |
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| author | Cevahir Yildirim Alba M. Franco-Pereira Rosa E. Lillo |
| author_facet | Cevahir Yildirim Alba M. Franco-Pereira Rosa E. Lillo |
| author_sort | Cevahir Yildirim |
| collection | DOAJ |
| description | Condition monitoring and fault classification in engineering systems is a critical challenge within the scope of Prognostics and Health Management (PHM). The fault diagnosis of complex nonlinear systems, such as hydraulic systems, has become increasingly important due to advancements in big data analytics, machine learning (ML), Industry 4.0, and Internet of Things (IoT) applications. Multi-sensor data provides opportunities to predict component conditions; however, environments characterized by multiple sensors and diverse fault states across various components complicate the fault classification process. To address these challenges, this study introduces a novel multivariate Functional Data Analysis (FDA) framework based on Multivariate Functional Principal Component Analysis (MFPCA) for classifying failure conditions in hydraulic systems. The proposed method systematically tackles condition-based diagnostics and addresses fundamental issues in multi-fault classification. Experimental results demonstrate that this approach achieves high classification accuracy using raw multi-sensor data, establishing multivariate FDA as a powerful tool for fault diagnosis in complex systems. |
| format | Article |
| id | doaj-art-612c8fcee7444aa8b8cb0605fa1bab5c |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-612c8fcee7444aa8b8cb0605fa1bab5c2025-01-17T04:50:39ZengElsevierHeliyon2405-84402025-01-01111e41251Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysisCevahir Yildirim0Alba M. Franco-Pereira1Rosa E. Lillo2uc3m - Santander Big Data Institute (IBiDat), Spain; Corresponding author.Interdisciplinary Mathematics Institute (IMI), UCM, Spain; Department of Statistics, UCM, Spainuc3m - Santander Big Data Institute (IBiDat), Spain; Department of Statistics, uc3m, SpainCondition monitoring and fault classification in engineering systems is a critical challenge within the scope of Prognostics and Health Management (PHM). The fault diagnosis of complex nonlinear systems, such as hydraulic systems, has become increasingly important due to advancements in big data analytics, machine learning (ML), Industry 4.0, and Internet of Things (IoT) applications. Multi-sensor data provides opportunities to predict component conditions; however, environments characterized by multiple sensors and diverse fault states across various components complicate the fault classification process. To address these challenges, this study introduces a novel multivariate Functional Data Analysis (FDA) framework based on Multivariate Functional Principal Component Analysis (MFPCA) for classifying failure conditions in hydraulic systems. The proposed method systematically tackles condition-based diagnostics and addresses fundamental issues in multi-fault classification. Experimental results demonstrate that this approach achieves high classification accuracy using raw multi-sensor data, establishing multivariate FDA as a powerful tool for fault diagnosis in complex systems.http://www.sciencedirect.com/science/article/pii/S2405844024172829Functional data analysis (FDA)Multivariate functional principal component analysis (MFPCA)Fault diagnosis and classificationCondition monitoringHydraulic systems |
| spellingShingle | Cevahir Yildirim Alba M. Franco-Pereira Rosa E. Lillo Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis Heliyon Functional data analysis (FDA) Multivariate functional principal component analysis (MFPCA) Fault diagnosis and classification Condition monitoring Hydraulic systems |
| title | Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis |
| title_full | Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis |
| title_fullStr | Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis |
| title_full_unstemmed | Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis |
| title_short | Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis |
| title_sort | condition monitoring and multi fault classification of hydraulic systems using multivariate functional data analysis |
| topic | Functional data analysis (FDA) Multivariate functional principal component analysis (MFPCA) Fault diagnosis and classification Condition monitoring Hydraulic systems |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024172829 |
| work_keys_str_mv | AT cevahiryildirim conditionmonitoringandmultifaultclassificationofhydraulicsystemsusingmultivariatefunctionaldataanalysis AT albamfrancopereira conditionmonitoringandmultifaultclassificationofhydraulicsystemsusingmultivariatefunctionaldataanalysis AT rosaelillo conditionmonitoringandmultifaultclassificationofhydraulicsystemsusingmultivariatefunctionaldataanalysis |