AUTHOR=Lagare Rexonni B. , Gonzalez Marcial , Nagy Zoltan K. , Reklaitis Gintaras V. 

TITLE=A framework for the practical development of condition monitoring systems with application to the roller compactor

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1351665

DOI=10.3389/fenrg.2024.1351665

ISSN=2296-598X

ABSTRACT=Implementing a condition-based maintenance strategy requires an effective condition monitoring (CM) system that can be complicated to develop and even harder to maintain. In this paper, we review the main complexities of developing condition monitoring systems and introduce a four-stage framework that can address some of these difficulties. The framework achieves this by first using process knowledge to create a representation of the process condition. This representation can be broken down into simpler modules, allowing existing monitoring systems to be mapped to their corresponding module. Data-driven models such as machine learning models could then be used to train the modules that do not have existing CM systems.Even though data-driven models tend to not perform well with limited data, which is commonly the case in the early stages of pharmaceutical process development, application of this framework to a pharmaceutical roller compaction unit shows that the machine learning models trained on the simpler modules can make accurate predictions with novel fault detection capabilities. This is attributed to the incorporation of process knowledge to distill the process signals to the most important ones vis-à-vis the faults under consideration. Furthermore, the framework allows the holistic integration of these modular CM systems, which further extend their individual capabilities by maintaining process visibility during sensor maintenance.Abnormal conditions in pharmaceutical manufacturing need to be corrected before they can degenerate further and start compromising product quality, equipment health, and operator safety. Without timely intervention of these faulty conditions, operators could be forced to perform costly product diversions and process shutdowns; and if they happen frequently enough, they could negatively offset any potential benefit of shifting pharmaceutical manufacturing from batch to continuous mode. (Ganesh et al., 2020;Lee et al., 2015;Schenkendorf, 2016) Maintaining steady-state operation for continuous systems thus requires not only effective process control but also real-time monitoring of the system condition; faults need to be detected and diagnosed promptly so that appropriate maintenance activities can be promptly performed.