Journal
ALEXANDRIA ENGINEERING JOURNAL
Volume 76, Issue -, Pages 525-541Publisher
ELSEVIER
DOI: 10.1016/j.aej.2023.06.0261110-0168
Keywords
Diesel Generator (DG); Predictive maintenance; IoT; CMS; Industry 4; 0; Remote monitoring; Decision Support System (DSS)
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Diesel generators are a reliable backup power source, and their efficiency can be improved by monitoring key machine parameters. Traditional equipment maintenance methods have been replaced by IoT-based remote monitoring systems. This paper introduces a remote monitoring and data acquisition scheme for predictive maintenance, discusses a strategy for real-time observation and comprehensive analysis of DG parameters, and includes a monitoring and analysis scheme for crucial factors such as engine speed, voltage output, current production, power factor, coolant requirement, fuel consumption, and battery health.
In most business and residential organizations, Diesel Generators (DG) is a viable supplementary power source for ensuring an undisturbed power supply. The DG is a hybrid machine that generates electrical energy using a Diesel Engine (DE) and an Electric Generator (EG). By routinely monitoring crucial machine parameters, alternative power source efficiency can be improved. Furthermore, Condition Monitoring Systems (CMS) based on the Internet of Things (IoT) have supplanted the traditional equipment maintenance method. Predictive maintenance is also an important building block of Industry 4.0, whose entire process and performance can be fully understood by using IoT-enabled Remote Monitoring (RM) schemes. Firstly, this paper introduces a remote monitoring and data acquisition scheme to realize the concept of predictive maintenance. Secondly, this article discusses a strategy for real-time observation of DG parameters as well as a comprehensive analysis of various metrics. Thirdly, this research article includes a monitoring and analysis scheme of crucial factors in a DG, like the speed of an engine, voltage output, the current produced, power factor, coolant required, fuel consumption, and battery health. Different mathematical models are formulated by correlating experimental data and estimating the coeffi
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