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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.
Autonomous Fault Monitoring for Efficient Multi-Actuator Compressed Air Systems: Data Analytics of Demand-Oriented Parameters
Studies covering pneumatic fault monitoring typically perform research on simple systems, investigating the effects on a single actuator or a very small system. This study aimed at tackling this issue, with tests performed on an industrial multi-actuator pick-and-place setup, logging data (i.e. cycle time, flow rate and system pressure) concurrently at two locations within the system. Furthermore, different sized leaks were introduced at three distinct locations, while monitoring their impacts on the system. It was found that with the use of the average, standard deviation and impulse factor of the cycle time and the other two parameters, it was possible to identify the presence of faults on a relatively large system. For instance, the retraction time for one of the actuators reduced by 26% as one of the faults was induced. With modern industrial setups already logging the cycle time and system pressure along the demand-side, this study shows that by using existing equipment, one can develop a reliable fault monitoring system.
Implementation of an intelligence-based framework for anomaly detection on the demand-side of sustainable compressed air systems
The implementation of intelligent techniques produces good results in automating fault finding and predicting future outcomes. These approaches have been on the increase in the past years, especially so to detect faults within Compressed Air Systems (CASs). With the use of intelligent techniques, one could minimise the manual and time-consuming aspect of CAS maintenance, improve the environmental impact of the system, while minimising downtime. This paper proposes a general framework for the implementation of intelligent analysis techniques within a real-world system. Such an approach has been implemented on the demand-side of a CAS. In literature, no open datasets are available for use by artificial intelligence models. Hence, as part of this research, a fault generating and monitoring system has been connected to an existing production machine in a manufacturing site to collect the required data. Two classification machine learning methods were implemented and compared across a number of performance metrics.