Machine learning and reasoning for predictive maintenance in Industry 4.0: current status and challenges
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Machine learning algorithms can help robots to predict when maintenance or repairs are required, leading to reduced downtime and cost savings.
“Predictive Maintenance: Machine learning algorithms can help robots to predict when maintenance or repairs are required, leading to reduced downtime and cost savings [50] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [50]
Cites
Systematic literature reviews in software engineering – A tertiary study
Kitchenham, B., Pretorius, R., Budgen, D., Pearl Brereton, O., Turner, M., Niazi, M., & Linkman, S. (2010) · Information and Software Technology · doi:10.1016/j.infsof.2010.03.006 · Cited by 1,230 · see citing works
Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics
Bumblauskas, D., Gemmill, D., Igou, A., & Anzengruber, J. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.08.025 · Cited by 120 · see citing works
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Artificial intelligence, machine learning and deep learning in advanced robotics, a review
Soori, M., Arezoo, B., & Dastres, R. (2023) · Cognitive Robotics · doi:10.1016/j.cogr.2023.04.001 · Cited by 1,131 · see citing works
Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023) · Energies · doi:10.3390/en16104025 · Cited by 237 · see citing works
Herrmann, T., & Pfeiffer, S. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01391-5 · Cited by 173 · see citing works
“The use of machine learning in PM is a hot topic with a number of unresolved issues, such as scalability, latency, and data security (Dalzochio et al. 2020).”
BackgroundA systematic review on semantic interoperability in the IoE-enabled smart cities
Pliatsios, A., Kotis, K., & Goumopoulos, C. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100754 · Cited by 125 · see citing works
Variable time delay estimation in continuous industrial processes
Cattaldo, M., Ferrer, A., & Måge, I. (2024) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2024.105082 · Cited by 18 · see citing works
- Authors
- Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., & Barbosa, J.
- Affiliations
- Universidade do Vale do Rio dos Sinos (BR)
- Universidade Federal do Rio Grande do Sul (BR)
- IBM (United States) (US)
- Corresponding author
- Kunst, Rafael
- Venue
- Computers in Industry
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 123
- Pages
- 103298
- ISSN
- 0166-3615 (ISSN-L)
- OpenAlex
- W3082233933
- Cited by
- 621
- References
- 2
- Citations by year
- 2026: 111 · 2025: 116 · 2024: 122 · 2023: 118 · 2022: 99 · 2021: 53 · 2020: 2
- FWCI
- 58.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Industry (publishedVersion)
- Funders
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- predictive maintenance, Industry 4.0, failure prediction, machine learning, reasoning, ontology (information science), decision support system, design support system, research challenges
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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