Navy vessels rely on proper functioning of their Hull, Mechanical, and Electrical (HM&E) systems to perform their missions. However, faults and errors can occur unexpectedly, causing downtime and potentially hazardous situations. To address this issue, TDI Novus proposes PROPEL to detect, analyze, and classify faults and errors in real-time using probabilistic fault models trained on historical sensor data. The system can provide timely alerts to the autonomy software, off-board operators, or onboard engineers. Further, PROPEL autonomously creates resolution strategies to correct for these faults. The use of machine learning techniques enables the system to continuously improve its performance and accuracy over time. Once a fault has been detected, the system can autonomously find a plan of action to resolve the issue using a self-improving automated planning agent. The agent takes into account various factors such as the severity of the fault, availability of resources, and operational constraints to come up with the best course of action. Additionally, PROPEL can continuously learn new plans using an in-system simulation process, which allows it to adapt to changing conditions and improve its decision-making capabilities. The combination of real-time fault detection, automated planning, and continuous learning makes this system a valuable tool for maintaining the operational readiness of Navy ships.
Benefit: PROPEL technology has several potential commercial benefits for a variety of industries, including manufacturing, energy, and transportation. One of the key benefits is increased efficiency. The real-time fault detection and automated planning capabilities of the system can help minimize downtime and reduce the time and resources required to resolve issues, leading to overall improvements in system efficiency. Additionally, the technology can help companies save costs. By reducing downtime and minimizing the need for manual intervention, the system can help reduce maintenance costs and improve asset utilization. Moreover, the system can use historical sensor data to predict potential faults and failures before they occur, enabling proactive maintenance and reducing the likelihood of costly unplanned downtime. Finally, companies that adopt this technology may gain a competitive advantage over those that rely on manual methods of fault detection and resolution. They can operate more efficiently, effectively, and safely, thereby differentiating themselves in the marketplace and potentially attracting more business.
Keywords: Fault Identification, Fault Identification, Ship Domain Controller, Machine Learning, Hull Mechanical and Electrical, Controls Automation
