An efficient way to enable prognostics in an onboard system

Prognostics and Health Management (PHM) systems are becoming increasingly important for monitoring and maintaining high value assets. In order to enable real time onboard diagnostic and prognostic capabilities, mechanisms for reading, manipulating and analyzing the data need to be architected into the onboard system. Machine learning and statistical algorithms provide tools to develop data models for enabling prognostics that are typically developed off-board by mining historical data. Once trained, the logic of processing real time data is then embedded on a real time onboard system. A straightforward approach for incorporating the knowledge and intelligence for real time data processing is to add the needed logic and algorithms as an integral part of the onboard software. While this method can serve the purpose of enabling real time health assessment and analysis, it is very restrictive in nature. Every time the analytics need to be updated or algorithms need refinement, it requires a refresh of the complete onboard software. The ability to fine tune onboard embedded logic for the purpose of making the analysis smarter is crucial for creating a successful and sound health monitoring system. In addition, it is desired that the process of encoding logic and algorithms should be simple and easy to incorporate into the system. User friendliness of the process of embedding intelligent logic is critical for long term maintenance of the system as well. This paper discusses an approach to build algorithms and logic into an onboard system such that they are programmatically decoupled from the onboard software. The approach described in this paper allows users the ease of use and flexibility in building knowledge into the system. In addition, as more historical data is collected and richer knowledge is discovered from mining the data, algorithms can be improved over time without having to update the onboard software.