ABSTRACT
DIAGNOSTICS AND MONITORING IN ELECTRO-MECHANICAL ASSEMBLIES: ASSESSING THE LATEST TOOLS AND TECHNIQUES FOR SYSTEM HEALTH PREDICTION
Acta Electronica Malaysia (AEM)
Author:Peter Efosa Ohenhen, Nwabueze Kelvin Nwaobia, Chinedu Nnamdi Nwasike, Joachim Osheyor Gidiagba, Emmanuel Chigozie Ani
This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
DOI :10.26480/aem.01.2024.11.20
The reliability and longevity of electro-mechanical assemblies are critical to the operational efficiency of a wide range of industries. Recent advancements in diagnostics and monitoring technologies have provided the potential to revolutionize the predictive maintenance landscape. This paper reviews the latest tools and techniques for system health prediction, including the deployment of advanced sensor technologies, non-destructive testing methods, machine learning algorithms, and the integration of the Internet of Things (IoT). The study assess the efficacy of these innovations in real-time and periodic monitoring strategies through comparative studies and case analyses. Furthermore, the study explore the challenges of integrating these technologies, such as technical limitations, interoperability, data management, and the balance between innovation and regulatory compliance. The economic and environmental impacts of implementing these advanced monitoring solutions are also examined, providing a cost-benefit analysis and discussing sustainability considerations. Looking forward, the review identify the technical and research gaps that must be addressed to enhance the diagnostic capabilities of electro-mechanical systems.
Pages | 11-20 |
Year | 2024 |
Issue | 1 |
Volume | 8 |