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| Format: | Preprint |
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2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.06993 |
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| _version_ | 1866914945191378944 |
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| author | Ma, Sizhe Flanigan, Katherine A. Bergés, Mario |
| author_facet | Ma, Sizhe Flanigan, Katherine A. Bergés, Mario |
| contents | Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance processes. Yet, PMx continues to face numerous limitations such as poor explainability, sample inefficiency of data-driven methods, complexity of physics-based methods, and limited generalizability and scalability of knowledge-based methods. This paper proposes leveraging Digital Twins (DTs) to address these challenges and enable automated PMx adoption on a larger scale. While DTs have the potential to be transformative, they have not yet reached the maturity needed to bridge these gaps in a standardized manner. Without a standard definition guiding this evolution, the transformation lacks a solid foundation for development. This paper provides a requirement-based roadmap to support standardized PMx automation using DT technologies. Our systematic approach comprises two primary stages. First, we methodically identify the Informational Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a foundation from which any unified framework must emerge. Our approach to defining and using IRs and FRs as the backbone of any PMx DT is supported by the proven success of these requirements as blueprints in other areas, such as product development in the software industry. Second, we conduct a thorough literature review across various fields to assess how these IRs and FRs are currently being applied within DTs, enabling us to identify specific areas where further research is needed to support the progress and maturation of requirement-based PMx DTs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_06993 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies Ma, Sizhe Flanigan, Katherine A. Bergés, Mario Artificial Intelligence Systems and Control Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance processes. Yet, PMx continues to face numerous limitations such as poor explainability, sample inefficiency of data-driven methods, complexity of physics-based methods, and limited generalizability and scalability of knowledge-based methods. This paper proposes leveraging Digital Twins (DTs) to address these challenges and enable automated PMx adoption on a larger scale. While DTs have the potential to be transformative, they have not yet reached the maturity needed to bridge these gaps in a standardized manner. Without a standard definition guiding this evolution, the transformation lacks a solid foundation for development. This paper provides a requirement-based roadmap to support standardized PMx automation using DT technologies. Our systematic approach comprises two primary stages. First, we methodically identify the Informational Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a foundation from which any unified framework must emerge. Our approach to defining and using IRs and FRs as the backbone of any PMx DT is supported by the proven success of these requirements as blueprints in other areas, such as product development in the software industry. Second, we conduct a thorough literature review across various fields to assess how these IRs and FRs are currently being applied within DTs, enabling us to identify specific areas where further research is needed to support the progress and maturation of requirement-based PMx DTs. |
| title | State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies |
| topic | Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2311.06993 |