IoT-Enabled Predictive Maintenance and Analytic Hierarchy Process Based Prioritization of Real-Time Parameters in a Diesel Generator: An Industry 4.0 Case Study

Diesel generator (DG) is a secondary power source that is becoming increasingly common for supplying continuous power to business and residential buildings. These hybrid machines provide the necessary electrical energy using both a diesel engine and an electric generator. Monitoring crucial machine characteristics on a regular basis is crucial for increasing their efficiency. condition monitoring systems (CMS) powered by the Internet of Things (IoT) have taken the role of Remote Monitoring (RM). Moreover, a key component of Industry 4.0 is predictive maintenance, which may be accomplished by implementing IoT-enabled RM. This paper portrays two aspects of the DG unit. Firstly, the performance analysis of the DG unit using a 4G-enabled IoT node installed on the motherboard. This research work develops and tests a framework for monitoring and analyzing key metrics, including engine speed, voltage and current generated, power factor, coolant, fuel, and battery health status. Secondly, the Analytic Hierarchy Process (AHP) can be used to rank the performance indicators that are most important for the efficient monitoring of the DG system in the context of IoT-enabled DG monitoring. The AHP technique enables decision-makers to objectively assess the relative weights of several criteria and to prioritize them. Using AHP, decision-makers can weigh each of these criteria based on how important they are in relation to one another. The performance of the DG system may then be assessed using these weights, and potential areas for improvement can be found.