Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review
In the modern age of digitalization, electronics are fundamental to any engineering system. With the current strong focus on the Internet of Things (IoT), autonomous vehicles and Industry 4.0, reliable electronics are gaining crucial importance. Predicting the health of complex systems is able to av...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S277267112300061X |
| _version_ | 1797802057703358464 |
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| author | Darshankumar Bhat Stefan Muench Mike Roellig |
| author_facet | Darshankumar Bhat Stefan Muench Mike Roellig |
| author_sort | Darshankumar Bhat |
| collection | DOAJ |
| description | In the modern age of digitalization, electronics are fundamental to any engineering system. With the current strong focus on the Internet of Things (IoT), autonomous vehicles and Industry 4.0, reliable electronics are gaining crucial importance. Predicting the health of complex systems is able to avoid catastrophic failures. Prognostic and Health Monitoring (PHM) approaches are an important step toward trustable and reliable electronics. Nowadays, Artificial Intelligence (AI) and machine learning (ML) algorithms are integrated into PHM approaches, enabling complex fault diagnosis. In this contribution, we provide an overview of the application of intelligent algorithms in PHM of electronics in a systematic manner. The challenges of prognostics in electronics are provided and a detailed overview of the available PHM precursors for various electronic components and the associated selection process is given. Based on the literature review conducted, the main research challenges with ML algorithms in PHM are discussed along with performances of each model. |
| first_indexed | 2024-03-13T05:00:57Z |
| format | Article |
| id | doaj.art-a935cb5b31af49d69588bcaed75dcd9c |
| institution | Directory Open Access Journal |
| issn | 2772-6711 |
| language | English |
| last_indexed | 2024-03-13T05:00:57Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj.art-a935cb5b31af49d69588bcaed75dcd9c2023-06-17T05:21:52ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112023-06-014100166Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A reviewDarshankumar Bhat0Stefan Muench1Mike Roellig2Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Dresden, Germany; Corresponding author.Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Dresden, GermanyFraunhofer Institute for Ceramic Technologies and Systems IKTS, Dresden, Germany; Dresden Center for Fatigue and Reliability (DCFR), 01062 Dresden, GermanyIn the modern age of digitalization, electronics are fundamental to any engineering system. With the current strong focus on the Internet of Things (IoT), autonomous vehicles and Industry 4.0, reliable electronics are gaining crucial importance. Predicting the health of complex systems is able to avoid catastrophic failures. Prognostic and Health Monitoring (PHM) approaches are an important step toward trustable and reliable electronics. Nowadays, Artificial Intelligence (AI) and machine learning (ML) algorithms are integrated into PHM approaches, enabling complex fault diagnosis. In this contribution, we provide an overview of the application of intelligent algorithms in PHM of electronics in a systematic manner. The challenges of prognostics in electronics are provided and a detailed overview of the available PHM precursors for various electronic components and the associated selection process is given. Based on the literature review conducted, the main research challenges with ML algorithms in PHM are discussed along with performances of each model.http://www.sciencedirect.com/science/article/pii/S277267112300061XElectronicsPrognostic and health monitoringReliabilityMachine learningPrecursorsRemaining useful lifetime |
| spellingShingle | Darshankumar Bhat Stefan Muench Mike Roellig Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review e-Prime: Advances in Electrical Engineering, Electronics and Energy Electronics Prognostic and health monitoring Reliability Machine learning Precursors Remaining useful lifetime |
| title | Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review |
| title_full | Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review |
| title_fullStr | Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review |
| title_full_unstemmed | Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review |
| title_short | Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review |
| title_sort | application of machine learning algorithms in prognostics and health monitoring of electronic systems a review |
| topic | Electronics Prognostic and health monitoring Reliability Machine learning Precursors Remaining useful lifetime |
| url | http://www.sciencedirect.com/science/article/pii/S277267112300061X |
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