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DC Field | Value | Language |
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dc.contributor.author | Mallia, Jasmine | - |
dc.contributor.author | Francalanza, Emmanuel | - |
dc.contributor.author | Xuereb, Peter Albert | - |
dc.contributor.author | Refalo, Paul | - |
dc.date.accessioned | 2023-08-01T06:11:49Z | - |
dc.date.available | 2023-08-01T06:11:49Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Mallia, J., Francalanza, E., Xuereb, P., & Refalo, P. (2023). Intelligent Approaches for Anomaly Detection in Compressed Air Systems: A Systematic Review. Machines, 11(7), 750. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/112150 | - |
dc.description.abstract | Inefficiencies within compressed air systems (CASs) call for the integration of Industry 4.0 technologies for financially viable and sustainable operations. A systematic literature review of intelligent approaches within CASs was carried out, in which the research methodology was based on the PRISMA guidelines. The search was carried out on 1 November 2022 within two databases: Scopus and Web of Science. The research methodology resulted in 37 papers eligible for a qualitative and bibliometric analysis based on a set of research questions. These aimed to identify specific characteristics of the selected publications. Thus, the review performed a comprehensive analysis on mathematical approaches, multiple machine learning (ML) methods, the implementation of neural networks (NNs), the development of time-series techniques, comparative analysis, and hybrid techniques. This systematic literature review allowed the comparison of these approaches, while widening the perspective on how such methods can be implemented within CASs for a more intelligent approach. Any limitations or challenges faced were mitigated through an unbiased procedure of involving multiple databases, search terms, and researchers. Therefore, this systematic review resulted in discussions and implications for the definition of future implementations of intelligent approaches that could result in sustainable CASs. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | MDPI | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Pneumatic control | en_GB |
dc.subject | Classification | en_GB |
dc.subject | Artificial intelligence -- Case studies | en_GB |
dc.subject | Sustainability -- Case studies | en_GB |
dc.title | Intelligent approaches for anomaly detection in compressed air systems : a systematic review | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.3390/machines11070750 | - |
dc.publication.title | Machines | en_GB |
Appears in Collections: | Scholarly Works - FacEngIME |
Files in This Item:
File | Description | Size | Format | |
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Intelligent_approaches_for_anomaly_detection_in_compressed_air_systems.pdf | 5.02 MB | Adobe PDF | View/Open |
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