A novel mathematical framework to support condition monitoring and condition based maintenance is presented in this study. The framework consists of a data flow path, i.e. from Industrial IoT (i.e. with Big Data) to advanced data analytics with digital models and that can be a part of industrial data handling processes. The digital models are derived from ship performance and navigation data sets and a combination of such models facilitates towards proposed data analytics. Since the respective data sets are used to derive these analytics, that can be a good representation of the respective systems under different modeling levels. Hence, this mathematical framework is also categorized as a reverse engineering approach. Furthermore, a data anomaly detection and recover procedure associated with the same framework to improve the respective data quality is also described in this study.
- Utilization in big data sets for condition monitoring and condition based maintenance
- Reverse engineering of systems up to component levels from big data
- Advanced data analytics with digital models for conditions monitoring
- Data anomaly detection and recovery from data analytics
- Visual analytics towards system health conditions