Cognitive Analysis and Machine Learning will Drive Change in the Maritime Industry
- Walter Mitchell (SparkCognition, Inc.)
- Document ID
- The Society of Naval Architects and Marine Engineers
- SNAME Maritime Convention, 24-28 October, Houston, Texas
- Publication Date
- Document Type
- Conference Paper
- 2017. The Society of Naval Architects and Marine Engineers
- 0 in the last 30 days
- 13 since 2007
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Rapidly developing technologies are transforming the way major transportation assets are managed. Innovations such as the cloud, cognitive analytics, the Industrial Internet of Things (IIoT), and advanced cybersecurity are showing emergent signs of improving operations of afloat, ashore, subsurface, and aloft transportation assets. In the maritime space, shipowners, operators, technical managers, OEMs of shipboard machinery, and associated regulatory entities are embracing these technologies in order to better understand the overall health of their machinery.
A technology that is becoming more and more available to the maritime sector is the capability to ingest massive amounts of operating data on machinery, including every aspect of a given sea passage, and analyze it with an eye to optimization. This is the evolution of data analytics—from the “days of yore” logging data in a logbook, to then detecting data with multiple sensors connected to a central console, to eventually transmitting supervised data ashore, to the latest development: a deep understanding of how the machine actually behaves.
These tools and their strategic uses allow the owners and operators of machines to assess machine conditions in real time, while returning actionable guidance to the operators. Most recently, the introduction of artificial intelligence (AI) and machine learning has dramatically expanded the toolbox for fleet managers, creating the most in-depth analysis available in any industrial market sector.
This paper addresses the maritime industry’s progression towards more proactive strategies and tools to monitor the health of shipboard machinery. It will also introduce the idea of AI-based predictive analytics, which use sophisticated algorithms combined with next generation AI-driven prognostics to allow an operator to see the current and future health of the machine as it operates in all states.
|File Size||903 KB||Number of Pages||10|