Survey of prognostics methods for condition-based maintenance in engineering systems Article Swipe
Ehsan Taheri
,
Ilya Kolmanovsky
,
Oleg Gusikhin
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1912.02708
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1912.02708
It is not surprising that the idea of efficient maintenance algorithms (originally motivated by strict emission regulations, and now driven by safety issues, logistics and customer satisfaction) has culminated in the so-called condition-based maintenance program. Condition-based program/monitoring consists of two major tasks, i.e., \textit{diagnostics} and \textit{prognostics} each of which has provided the impetus and technical challenges to the scientists and engineers in various fields of engineering. Prognostics deals with the prediction of the remaining useful life, future condition, or probability of reliable operation of an equipment based on the acquired condition monitoring data. This approach to modern maintenance practice promises to reduce the downtime, spares inventory, maintenance costs, and safety hazards. Given the significance of prognostics capabilities and the maturity of condition monitoring technology, there have been an increasing number of publications on machinery prognostics in the past few years. These publications cover a wide range of issues important to prognostics. Fortunately, improvement in computational resources technology has come to the aid of engineers by presenting more powerful onboard computational resources to make some aspects of these new problems tractable. In addition, it is possible to even leverage connected vehicle information through cloud-computing. Our goal is to review the state of the art and to summarize some of the recent advances in prognostics with the emphasis on models, algorithms and technologies used for data processing and decision making.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1912.02708
- https://arxiv.org/pdf/1912.02708
- OA Status
- green
- Cited By
- 8
- References
- 241
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2993027692
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2993027692Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1912.02708Digital Object Identifier
- Title
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Survey of prognostics methods for condition-based maintenance in engineering systemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-12-03Full publication date if available
- Authors
-
Ehsan Taheri, Ilya Kolmanovsky, Oleg GusikhinList of authors in order
- Landing page
-
https://arxiv.org/abs/1912.02708Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1912.02708Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1912.02708Direct OA link when available
- Concepts
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Prognostics, Downtime, Condition monitoring, Condition-based maintenance, Risk analysis (engineering), Reliability engineering, Computer science, Leverage (statistics), Predictive maintenance, Engineering, Systems engineering, Artificial intelligence, Business, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 1, 2021: 2, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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241Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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