MIE.39 – IIOT Preventative Maintenance
Team Members Heading link
- Jake Dangelo
- Yasmeen Mezies
- Skyla Rijos
- Reem Saeed
- Jack Wolowski
Project Description Heading link
The project seeks to develop a condition monitoring solution and create a Predictive Maintenance System (PMS) leveraging the capabilities of the Industrial Internet of Things (IIoT) to enhance operational efficiency and reduce machine downtime in the industrial sector. The sponsor, Signode, has requested the team develop a condition monitoring system and predictive maintenance system for the GCU SmartFlex-s and the SIG-VCS. The study investigates the connection between strap usage (measured in meters) or the number of successful product counts and the incidence of initial feed failures, first refeed (second attempt) failures, and second refeed (third attempt) failures. By identifying critical thresholds at which failures become more prevalent concerning successful product counts, the team aims to devise preemptive maintenance strategies. Moreover, the researchers compare the aforementioned metrics between different coils utilized by the machinery to uncover any disparities in failure rates. Emphasis is placed on analyzing the fail-to-strap-usage ratio to evaluate the efficiency of coil utilization and its influence on machine reliability. Additionally, the study explores the correlation between strap tension, as indicated on the Human-Machine Interface (HMI), and the occurrence of feed failures. By assessing how variations in strap tension relate to the frequency of failures, the researchers aim to determine optimal tension thresholds to mitigate the risk of feed failures during operation. Ultimately, this project aims to provide valuable insights into industrial processes, enabling informed decision-making to enhance system performance, reliability, and efficiency. Through meticulous analysis and experimentation, the researchers aspire to contribute to the advancement of manufacturing processes by implementing targeted interventions to address identified challenges and improve overall operational effectiveness.