Fault Detection in an Electro-Hydrostatic Actuator Using Polyscale Complexity Measures and Bayesian Classification
This article presents a novel approach for fault detection in a hydraulic actuation system. The fault of interest is the internal leakage of the actuator, which may often be caused by the wearing down of the piston seal. Bayesian classification and polyscale complexity measures are used in this arti...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Instrumentation and Measurement |
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| Online Access: | https://ieeexplore.ieee.org/document/10739666/ |
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| _version_ | 1841536215603478528 |
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| author | Soleiman Hosseinpour Witold Kinsner Saman Muthukumarana Nariman Sepehri |
| author_facet | Soleiman Hosseinpour Witold Kinsner Saman Muthukumarana Nariman Sepehri |
| author_sort | Soleiman Hosseinpour |
| collection | DOAJ |
| description | This article presents a novel approach for fault detection in a hydraulic actuation system. The fault of interest is the internal leakage of the actuator, which may often be caused by the wearing down of the piston seal. Bayesian classification and polyscale complexity measures are used in this article. Bayesian inference provides a probabilistic framework for classification that combines prior knowledge with observed data to update the probability distribution of the classification parameters. It results in a posterior distribution that reflects the updated knowledge. This allows for more accurate and reliable fault detection, especially in cases where the available data are uncertain or noisy. In order to extract features from the acquired signals, a polyscale measure known as variance fractal dimension (VFD) is employed. VFD measures are employed as features for Bayesian classification, allowing for distinguishing faulty conditions. The efficacy of the proposed method is demonstrated using experimental data, achieving an accuracy of 93.75%. Consequently, the proposed method is considered to be promising for fault detection in fluid power applications. |
| format | Article |
| id | doaj-art-ece48d8c2e2f442d8f612f76d752531b |
| institution | Kabale University |
| issn | 2768-7236 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Instrumentation and Measurement |
| spelling | doaj-art-ece48d8c2e2f442d8f612f76d752531b2025-01-15T00:04:19ZengIEEEIEEE Open Journal of Instrumentation and Measurement2768-72362024-01-01311310.1109/OJIM.2024.348723710739666Fault Detection in an Electro-Hydrostatic Actuator Using Polyscale Complexity Measures and Bayesian ClassificationSoleiman Hosseinpour0https://orcid.org/0000-0001-8135-5783Witold Kinsner1https://orcid.org/0000-0002-6759-1410Saman Muthukumarana2https://orcid.org/0000-0001-8942-5352Nariman Sepehri3https://orcid.org/0000-0002-6384-8776Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB, CanadaDepartment of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, CanadaDepartment of Statistics, University of Manitoba, Winnipeg, MB, CanadaDepartment of Mechanical Engineering, University of Manitoba, Winnipeg, MB, CanadaThis article presents a novel approach for fault detection in a hydraulic actuation system. The fault of interest is the internal leakage of the actuator, which may often be caused by the wearing down of the piston seal. Bayesian classification and polyscale complexity measures are used in this article. Bayesian inference provides a probabilistic framework for classification that combines prior knowledge with observed data to update the probability distribution of the classification parameters. It results in a posterior distribution that reflects the updated knowledge. This allows for more accurate and reliable fault detection, especially in cases where the available data are uncertain or noisy. In order to extract features from the acquired signals, a polyscale measure known as variance fractal dimension (VFD) is employed. VFD measures are employed as features for Bayesian classification, allowing for distinguishing faulty conditions. The efficacy of the proposed method is demonstrated using experimental data, achieving an accuracy of 93.75%. Consequently, the proposed method is considered to be promising for fault detection in fluid power applications.https://ieeexplore.ieee.org/document/10739666/Bayesian classificationfault detectionhydraulic actuation systeminternal leakagepolyscale complexity measures |
| spellingShingle | Soleiman Hosseinpour Witold Kinsner Saman Muthukumarana Nariman Sepehri Fault Detection in an Electro-Hydrostatic Actuator Using Polyscale Complexity Measures and Bayesian Classification IEEE Open Journal of Instrumentation and Measurement Bayesian classification fault detection hydraulic actuation system internal leakage polyscale complexity measures |
| title | Fault Detection in an Electro-Hydrostatic Actuator Using Polyscale Complexity Measures and Bayesian Classification |
| title_full | Fault Detection in an Electro-Hydrostatic Actuator Using Polyscale Complexity Measures and Bayesian Classification |
| title_fullStr | Fault Detection in an Electro-Hydrostatic Actuator Using Polyscale Complexity Measures and Bayesian Classification |
| title_full_unstemmed | Fault Detection in an Electro-Hydrostatic Actuator Using Polyscale Complexity Measures and Bayesian Classification |
| title_short | Fault Detection in an Electro-Hydrostatic Actuator Using Polyscale Complexity Measures and Bayesian Classification |
| title_sort | fault detection in an electro hydrostatic actuator using polyscale complexity measures and bayesian classification |
| topic | Bayesian classification fault detection hydraulic actuation system internal leakage polyscale complexity measures |
| url | https://ieeexplore.ieee.org/document/10739666/ |
| work_keys_str_mv | AT soleimanhosseinpour faultdetectioninanelectrohydrostaticactuatorusingpolyscalecomplexitymeasuresandbayesianclassification AT witoldkinsner faultdetectioninanelectrohydrostaticactuatorusingpolyscalecomplexitymeasuresandbayesianclassification AT samanmuthukumarana faultdetectioninanelectrohydrostaticactuatorusingpolyscalecomplexitymeasuresandbayesianclassification AT narimansepehri faultdetectioninanelectrohydrostaticactuatorusingpolyscalecomplexitymeasuresandbayesianclassification |