This paper discusses the initial stages of the development and implementation of a noninvasive digital twin for pedagogical purposes in digital manufacturing, focusing on predictive maintenance in an Industrial Internet of Things (IIoT) water bottling plant. The physical setup of the IIoT water bottling plant is detailed, emphasizing transparency with interconnected tanks and solenoids for visual accessibility. The incorporation of noninvasive sensors is discussed for monitoring water volume and optimizing flow rates. Challenges related to solenoid valve performance are addressed through the development of a digital twin, providing insights into process understanding, monitoring, and maintenance. The overall contribution lies in advancing digital twin technology education and enhancing the efficiency of remote laboratories in the context of digital manufacturing.