@article{GENERAL,
      recid = {11159},
      author = {Johnson, W and Walker, J},
      title = {RESILIENT: Advance a Ship's HM&E resiliency through  contextual information models and innovative ML/AI  analytics At-The-Edge},
      journal = {Conference Proceedings of iSCSS},
      address = {2024-11-05},
      number = {GENERAL},
      abstract = {Maritime supply chain disruptions over recent years  stemming from causes such as piracy, the COVID-19 pandemic,  blockage of the Suez Canal and the ongoing Red Sea crisis,  underscore the pressures on navies and commercial ships for  higher ship operational availability. Ships are sailing  longer distances, at higher speeds and in more challenging  environmental conditions. These settings are  consequentially increasing demands for more effective ship  machinery monitoring. However, although shipboard systems  generate more data than ever, using that information to  improve operational availability remains elusive; data from  a ship’s electrical auxiliary and main propulsion systems  are often disorganized, undefined, and not timely. Further,  data is sometimes undiscoverable and frequently unusable by  the ship’s information system to prevent or mitigate  equipment failures. Moreover, increasing demands for more  sophisticated analytics to improve machine reliability,  with likely thousands of data points per system without any  relationship model to help interpret this data continues to  make machinery monitoring efforts more complex and costly.  Creating the datasets is just one piece of the puzzle.  These data points take on different meanings dependent on  grouping. The lack of consistent data requirements  definitions and context from one information system to  another introduces other challenges to integrating  machinery room operational data into the ship’s  higher-level information system and further up into an  organization’s fleet maintenance center. To this end,  this paper explores two evolving areas of technology: 1)  Machine learning schema for common hull, mechanical, and  electrical system machinery equipment to improve  contextualizing performance anomalies and that  equipment’s baseline operations and 2) an AI Information  model for machinery equipment that could advance the  ability of crews to reduce unplanned failures, increase  availability, and obtain an accurate representation of the  ship’s readiness state. These activities will drive  improved reliability, maintainability, and supportability  of these systems and a higher readiness for a propulsion  plant, electrical plant, damage control system, and  ship’s auxiliaries.},
      url = {http://library.imarest.org/record/11159},
      doi = {https://doi.org/10.24868/11159},
}