Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities
Integrating Machine Learning (ML) in industrial settings has become a cornerstone of Industry 4.0, aiming to enhance production system reliability and efficiency through Real-Time Fault Detection and Diagnosis (RT-FDD). This paper conducts a comprehensive literature review of ML-based RT-FDD. Out of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/60 |
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| author | Denis Leite Emmanuel Andrade Diego Rativa Alexandre M. A. Maciel |
| author_facet | Denis Leite Emmanuel Andrade Diego Rativa Alexandre M. A. Maciel |
| author_sort | Denis Leite |
| collection | DOAJ |
| description | Integrating Machine Learning (ML) in industrial settings has become a cornerstone of Industry 4.0, aiming to enhance production system reliability and efficiency through Real-Time Fault Detection and Diagnosis (RT-FDD). This paper conducts a comprehensive literature review of ML-based RT-FDD. Out of 805 documents, 29 studies were identified as noteworthy for presenting innovative methods that address the complexities and challenges associated with fault detection. While ML-based RT-FDD offers different benefits, including fault prediction accuracy, it faces challenges in data quality, model interpretability, and integration complexities. This review identifies a gap in industrial implementation outcomes that opens new research opportunities. Future Fault Detection and Diagnosis (FDD) research may prioritize standardized datasets to ensure reproducibility and facilitate comparative evaluations. Furthermore, there is a pressing need to refine techniques for handling unbalanced datasets and improving feature extraction for temporal series data. Implementing Explainable Artificial Intelligence (AI) (XAI) tailored to industrial fault detection is imperative for enhancing interpretability and trustworthiness. Subsequent studies must emphasize comprehensive comparative evaluations, reducing reliance on specialized expertise, documenting real-world outcomes, addressing data challenges, and bolstering real-time capabilities and integration. By addressing these avenues, the field can propel the advancement of ML-based RT-FDD methodologies, ensuring their effectiveness and relevance in industrial contexts. |
| format | Article |
| id | doaj-art-6206b0ee75d9403c90c621d418cb75ef |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-6206b0ee75d9403c90c621d418cb75ef2025-01-10T13:20:44ZengMDPI AGSensors1424-82202024-12-012516010.3390/s25010060Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and OpportunitiesDenis Leite0Emmanuel Andrade1Diego Rativa2Alexandre M. A. Maciel3Mekatronik I.C. Automacao Ltda, Rua Sargento Silvino Macedo, 130—Imbiribeira, Recife 51160-060, PE, BrazilInstituto de Inovação Tecnológica—IIT, Universidade de Pernambuco—UPE R. Min. Mario Andreaza, s/n—Várzea, Recife 50950-050, PE, BrazilInstituto de Inovação Tecnológica—IIT, Universidade de Pernambuco—UPE R. Min. Mario Andreaza, s/n—Várzea, Recife 50950-050, PE, BrazilInstituto de Inovação Tecnológica—IIT, Universidade de Pernambuco—UPE R. Min. Mario Andreaza, s/n—Várzea, Recife 50950-050, PE, BrazilIntegrating Machine Learning (ML) in industrial settings has become a cornerstone of Industry 4.0, aiming to enhance production system reliability and efficiency through Real-Time Fault Detection and Diagnosis (RT-FDD). This paper conducts a comprehensive literature review of ML-based RT-FDD. Out of 805 documents, 29 studies were identified as noteworthy for presenting innovative methods that address the complexities and challenges associated with fault detection. While ML-based RT-FDD offers different benefits, including fault prediction accuracy, it faces challenges in data quality, model interpretability, and integration complexities. This review identifies a gap in industrial implementation outcomes that opens new research opportunities. Future Fault Detection and Diagnosis (FDD) research may prioritize standardized datasets to ensure reproducibility and facilitate comparative evaluations. Furthermore, there is a pressing need to refine techniques for handling unbalanced datasets and improving feature extraction for temporal series data. Implementing Explainable Artificial Intelligence (AI) (XAI) tailored to industrial fault detection is imperative for enhancing interpretability and trustworthiness. Subsequent studies must emphasize comprehensive comparative evaluations, reducing reliance on specialized expertise, documenting real-world outcomes, addressing data challenges, and bolstering real-time capabilities and integration. By addressing these avenues, the field can propel the advancement of ML-based RT-FDD methodologies, ensuring their effectiveness and relevance in industrial contexts.https://www.mdpi.com/1424-8220/25/1/60fault detectionfault diagnosisintelligent manufacturing systemsmachine learningsmart manufacturing |
| spellingShingle | Denis Leite Emmanuel Andrade Diego Rativa Alexandre M. A. Maciel Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities Sensors fault detection fault diagnosis intelligent manufacturing systems machine learning smart manufacturing |
| title | Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities |
| title_full | Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities |
| title_fullStr | Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities |
| title_full_unstemmed | Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities |
| title_short | Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities |
| title_sort | fault detection and diagnosis in industry 4 0 a review on challenges and opportunities |
| topic | fault detection fault diagnosis intelligent manufacturing systems machine learning smart manufacturing |
| url | https://www.mdpi.com/1424-8220/25/1/60 |
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