التفاصيل البيبلوغرافية
| العنوان: |
DataSheet1_A framework for the practical development of condition monitoring systems with application to the roller compactor.docx |
| المؤلفون: |
Rexonni B. Lagare, Marcial Gonzalez, Zoltan K. Nagy, Gintaras V. Reklaitis |
| سنة النشر: |
2024 |
| المجموعة: |
Frontiers: Figshare |
| مصطلحات موضوعية: |
Nuclear Engineering, Carbon Sequestration Science, Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels), Carbon Capture Engineering (excl. Sequestration), Non-automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels), Chemical Engineering not elsewhere classified, Power and Energy Systems Engineering (excl. Renewable Power), Renewable Power and Energy Systems Engineering (excl. Solar Cells), Energy Generation, Conversion and Storage Engineering, Nuclear Engineering (incl. Fuel Enrichment and Waste Processing and Storage), Chemical Sciences not elsewhere classified, condition-monitoring, fault detection and diagnosis, condition-based maintenance, continuous pharmaceutical manufacturing, oral solid dosage, model-based machine learning, machine learning |
| الوصف: |
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. |
| نوع الوثيقة: |
dataset |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/dataset/DataSheet1_A_framework_for_the_practical_development_of_condition_monitoring_systems_with_application_to_the_roller_compactor_docx/25591923 |
| DOI: |
10.3389/fenrg.2024.1351665.s001 |
| الاتاحة: |
https://doi.org/10.3389/fenrg.2024.1351665.s001
https://figshare.com/articles/dataset/DataSheet1_A_framework_for_the_practical_development_of_condition_monitoring_systems_with_application_to_the_roller_compactor_docx/25591923 |
| Rights: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.6B82A9C0 |
| قاعدة البيانات: |
BASE |