The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Predictive Maintenance and Intelligent Sensors in Smart Factory: Review
Open accessAbstract
With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems’ decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper’s main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.
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Software survey: VOSviewer, a computer program for bibliometric mapping
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Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
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Hydrogels in Biology and Medicine: From Molecular Principles to Bionanotechnology
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A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis
Pantelopoulos, A., & Bourbakis, N. G. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2009.2032660 · Cited by 2,296
Support vector machine in machine condition monitoring and fault diagnosis
Widodo, A., & Yang, B.-S. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.12.007 · Cited by 1,650
Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016) · International Journal of Production Economics · doi:10.1016/j.ijpe.2016.03.014 · Cited by 1,545
Industry 4.0 Concept: Background and Overview
Rojko, A. (2017) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v11i5.7072 · Cited by 1,023
Fusion of Smartphone Motion Sensors for Physical Activity Recognition
Shoaib, M., Bosch, S., Incel, O., Scholten, H., & Havinga, P. (2014) · Sensors · doi:10.3390/s140610146 · Cited by 552
Systematic Reviews: Constructing a Search Strategy and Searching for Evidence
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Cited by
On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022) · Applied Sciences · doi:10.3390/app12168081 · Cited by 528
Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework
Sathupadi, K., Achar, S., Bhaskaran, S. V., Faruqui, N., Abdullah-Al-Wadud, M., & Uddin, J. (2024) · Sensors · doi:10.3390/s24247918 · Cited by 89
Predictive maintenance: A comprehensive analysis and future outlook
Patel, M., Vasa, J., & Patel, B. (2023) · 2023 2nd International Conference on Futuristic Technologies (INCOFT) · doi:10.1109/incoft60753.2023.10425122 · Cited by 18
Metadata
- Authors
- Pech, M., Vrchota, J., & Bednář, J.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-02-20
- Volume
- 21
- Issue
- 4
- Pages
- 1470
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W3131785880
- Cited by
- 461
- References
- 9
- FWCI
- 47.81
- Type
- journal article
- Language
- en
- Open access
- gold
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
