Approaches to Dealing With Missing Data in Railway Asset Management Article Swipe
Paul E. McMahon
,
Tieling Zhang
,
Richard Dwight
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2978902
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2978902
The collection of reliable and high-quality data is seen as a prerequisite for effective and efficient rail infrastructure and rolling stock asset management to meet the requirements of asset owners and service providers. In this paper, the importance of recovering missing information in railway asset management is highlighted, and the advanced models and algorithms that have been applied to recovering the missed data are analyzed and discussed. Through making comparisons among these models and algorithms, a procedure is proposed to guide selecting the appropriate models based on different data missing scenarios. Using the newly developed framework with one dataset from each scenario, new models with different structures are trained and finally, the most suitable model is selected and utilized to recover the missing data and the selected model's performance is evaluated using the data with known or clearly identified missing data mechanisms. Challenges via application of advanced algorithms for recovering missing data are discussed.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.2978902
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09026926.pdf
- OA Status
- gold
- Cited By
- 17
- References
- 126
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3009955828
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3009955828Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2020.2978902Digital Object Identifier
- Title
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Approaches to Dealing With Missing Data in Railway Asset ManagementWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
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2020-01-01Full publication date if available
- Authors
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Paul E. McMahon, Tieling Zhang, Richard DwightList of authors in order
- Landing page
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https://doi.org/10.1109/access.2020.2978902Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/09026926.pdfDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/09026926.pdfDirect OA link when available
- Concepts
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Missing data, Computer science, Asset management, Data mining, Data quality, Asset (computer security), Data collection, Data modeling, Data science, Service (business), Machine learning, Database, Computer security, Finance, Business, Marketing, Statistics, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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17Total citation count in OpenAlex
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2025: 2, 2024: 2, 2023: 1, 2022: 6, 2021: 5Per-year citation counts (last 5 years)
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126Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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