Cross-Method Overview of Fleet-Based Machine Health Estimation and Prediction: A Practical Guide for Industrial Applications
A reliable assessment of industrial machine health is crucial for economical and safe operation. To this end, data-driven approaches have gained prominence owing to the advancements in data acquisition and machine learning techniques. However, practical applications of these approaches often confron...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10945823/ |
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| author | Xuqian Yan Janis Woelke Boris Bensmann Christoph Eckert Richard Hanke-Rauschenbach Astrid Niebe |
| author_facet | Xuqian Yan Janis Woelke Boris Bensmann Christoph Eckert Richard Hanke-Rauschenbach Astrid Niebe |
| author_sort | Xuqian Yan |
| collection | DOAJ |
| description | A reliable assessment of industrial machine health is crucial for economical and safe operation. To this end, data-driven approaches have gained prominence owing to the advancements in data acquisition and machine learning techniques. However, practical applications of these approaches often confront the challenge of data scarcity, due to heterogeneity among machines. To address the data scarcity problem, this study delves into health estimation and prediction methods that utilize fleet data. Unlike existing review papers that mainly focus on one specific fleet-based method, this work offers a cross-method overview. The methods are classified into six categories. All share three steps: data selection, model development, and model adjustment. This work also provides a step-by-step guide for industry practitioners to incorporate fleet knowledge, which emphasizes business requirements and highlights an iterative method development process. It helps industrial practitioners navigate through the complexities of various approaches to utilize fleet knowledge, paving the way to bring advanced methods to industrial implementations. |
| format | Article |
| id | doaj-art-d8895cb9c2ba4d07a350ff9a6722b66d |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d8895cb9c2ba4d07a350ff9a6722b66d2025-08-20T02:19:48ZengIEEEIEEE Access2169-35362025-01-0113601316014710.1109/ACCESS.2025.355625110945823Cross-Method Overview of Fleet-Based Machine Health Estimation and Prediction: A Practical Guide for Industrial ApplicationsXuqian Yan0https://orcid.org/0000-0002-0578-4225Janis Woelke1https://orcid.org/0009-0009-6374-2531Boris Bensmann2https://orcid.org/0000-0001-8685-7192Christoph Eckert3https://orcid.org/0000-0002-1266-8660Richard Hanke-Rauschenbach4https://orcid.org/0000-0002-1958-307XAstrid Niebe5https://orcid.org/0000-0003-1881-9172Siemens Energy Global GmbH & Co., KG, Munich, GermanyInstitute of Electric Power Systems, Leibniz University Hannover, Hanover, GermanyInstitute of Electric Power Systems, Leibniz University Hannover, Hanover, GermanyInstitute of Electric Power Systems, Leibniz University Hannover, Hanover, GermanyInstitute of Electric Power Systems, Leibniz University Hannover, Hanover, GermanyDepartment of Computing Science, Carl von Ossietzky University of Oldenburg, Oldenburg, GermanyA reliable assessment of industrial machine health is crucial for economical and safe operation. To this end, data-driven approaches have gained prominence owing to the advancements in data acquisition and machine learning techniques. However, practical applications of these approaches often confront the challenge of data scarcity, due to heterogeneity among machines. To address the data scarcity problem, this study delves into health estimation and prediction methods that utilize fleet data. Unlike existing review papers that mainly focus on one specific fleet-based method, this work offers a cross-method overview. The methods are classified into six categories. All share three steps: data selection, model development, and model adjustment. This work also provides a step-by-step guide for industry practitioners to incorporate fleet knowledge, which emphasizes business requirements and highlights an iterative method development process. It helps industrial practitioners navigate through the complexities of various approaches to utilize fleet knowledge, paving the way to bring advanced methods to industrial implementations.https://ieeexplore.ieee.org/document/10945823/Fleet knowledgeindustrial applicationhealth estimationhealth predictiontransfer learning |
| spellingShingle | Xuqian Yan Janis Woelke Boris Bensmann Christoph Eckert Richard Hanke-Rauschenbach Astrid Niebe Cross-Method Overview of Fleet-Based Machine Health Estimation and Prediction: A Practical Guide for Industrial Applications IEEE Access Fleet knowledge industrial application health estimation health prediction transfer learning |
| title | Cross-Method Overview of Fleet-Based Machine Health Estimation and Prediction: A Practical Guide for Industrial Applications |
| title_full | Cross-Method Overview of Fleet-Based Machine Health Estimation and Prediction: A Practical Guide for Industrial Applications |
| title_fullStr | Cross-Method Overview of Fleet-Based Machine Health Estimation and Prediction: A Practical Guide for Industrial Applications |
| title_full_unstemmed | Cross-Method Overview of Fleet-Based Machine Health Estimation and Prediction: A Practical Guide for Industrial Applications |
| title_short | Cross-Method Overview of Fleet-Based Machine Health Estimation and Prediction: A Practical Guide for Industrial Applications |
| title_sort | cross method overview of fleet based machine health estimation and prediction a practical guide for industrial applications |
| topic | Fleet knowledge industrial application health estimation health prediction transfer learning |
| url | https://ieeexplore.ieee.org/document/10945823/ |
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