Challenges, Limitations, and Strengths for Optimal Predictive Maintenance Application
Abstract
Industry 4.0, the fourth industrial revolution, has emerged as the most recent digital transformation worldwide, expanding and reshaping the manufacturing industry by introducing novel technologies. In Industry 4.0, Smart Manufacturing (SM) and the Internet of Things (IoT) have collaborated to bring the best of both worlds and make the new manufacturing era more cost-effective, automated, and digitized. As a result, many businesses are putting sensors, intricate networks of integrated systems, big data analytics, cloud computing, and storage in place to use predictive maintenance (PdM) best. PdM uses IoT to convert physical activities into digital activities, also known as digitization. Predictive engineering has received the most academic attention of the six pillars of SM, demonstrating the need for complete integration of these technologies to improve data-driven decision-making. In this research, we discuss the relevance of PdM and its challenges, limitations, and strengths for optimal PdM applications. In addition to our primary analysis, we created an application case to show how the principles of SM, IoT, and Industry 4.0 fit into the broad and robust PdM technology. Using assets such as temperature sensors, microprocessors, network antennas, and software, a remote monitoring system and the “PAInOuTT” model application flow were created to fully understand the framework and process behind applying a PdM model in a manufacturing machine from a local industry. Furthermore, this study provided a unique opportunity to comprehend the challenges and expertise encountered by small businesses attempting to integrate IoT and SM technologies into their operational systems with limited resources.
Subject Area
Engineering|Industrial engineering|Computer Engineering
Recommended Citation
Rosales Cepeda, Erick Armando, "Challenges, Limitations, and Strengths for Optimal Predictive Maintenance Application" (2023). ETD Collection for University of Texas, El Paso. AAI30494375.
https://scholarworks.utep.edu/dissertations/AAI30494375