Failure Detection Techniques on the Demand Side of Smart and Sustainable Compressed Air Systems: A Systematic Review
This paper offers a novel review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology of fault detection methods on the demand side of compressed air systems, leading towards a comprehensive understanding of smart and sustainable pneumatic systems. Fifty-three studies were classified and reviewed under the following three areas: (a) demand parameters which help in identifying fault sources; (b) approaches taken to analyse the parametric data; and (c) the role of Artificial Intelligence (AI) in pneumatic fault monitoring systems. This review shows that fault detection on the demand side has received greater importance in the last five years and that data analysis is crucial for AI to be implemented correctly.
Intelligent Approaches for Anomaly Detection in Compressed Air Systems: A Systematic Review
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.