Real Options for Maintenance Scheduling with Bearing Degradation in Panel Manufacturer
Journal
2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)
Part Of
IEEE 20th International Conference on Automation Science and Engineering (IEEE CASE 2024)
Start Page
1325
End Page
1330
ISBN
9798350358513
Date Issued
2024-08-28
Author(s)
Abstract
This study focuses on improving maintenance strategies in the competitive manufacturing sector, emphasizing the role of predictive maintenance (PdM) through sensor data and the creation of health indicator (HI) for maintenance planning. The study analyzes vibration signals from aging bearings in a panel manufacturer. By extracting and selecting key features, it identifies crucial time- and frequency-domain characteristics to develop HI. We apply principal component analysis HI construction. The indicator is used for evaluating machinery health. The study employs the exponential Wiener process (EWP) to address the complexities and uncertainties of aging equipment. Using a real options framework and Monte Carlo simulation, the study can generate the benefits of extended operation and establish a conservative health diagnosis. The result provides optimal maintenance time, reducing resource waste from premature scheduling and ensuring continuous machinery functionality.
Event(s)
IEEE International Conference on Automation Science and Engineering
Subjects
health indicator
maintenance scheduling
predictive maintenance
real options
remaining useful life
stochastic processes
SDGs
Publisher
IEEE
Description
IEEE 20th International Conference on Automation Science and Engineering (IEEE CASE 2024), August 28 – September 1, 2024, Bari, Italy.
Type
conference paper