The Navy is looking for new machine learning methods to improve fleet management, conduct passive surveillance, and perform target identification. Conventional machine learning methods leverage neural networks, SVMs, or a random forest approach. These are not the only methods. Oracle has developed an industry-leading prognostic algorithm called the Multivariate State Estimation Technique (MSET) to perform prognostic analysis used in fleet management, passive surveillance, and target identification. Oracle’s algorithm can be deployed on-premises, in the cloud, or at the edge, and it can be delivered in a user-friendly package for operators without the need for formal training. MSET is an advanced prognostic pattern recognition method that was originally developed by the DOE for high-sensitivity prognostic fault monitoring applications in commercial nuclear power and aerospace applications. Over the last 20 years, Oracle has pioneered a suite of intelligent-data-preprocessing (IDP) algorithms and automated tuning and sensitivity-optimization algorithms for a second generation MSET2, which possesses significant advantages over conventional ML algorithmic approaches, including neural nets. MSET2 advantages include higher prognostic accuracy, earlier warning of incipient anomalies in complex/dynamic/chaotic time-series signatures, lower false-alarm and missed-alarm probabilities, and much lower overhead compute cost, which is crucial for real-time dense-sensor streaming prognostics. MSET2 has been used in a broad range of safety-critical, mission-critical, and business applications for anomaly detection, early warning, counterfeit-component detection, and passive surveillance. Applied examples include NASA space shuttles, naval ship-propulsion prognostics, Oil-and-Gas exploration predictive and prescriptive maintenance, human-in-the-loop supervisory control, prognostic cyber security for SCADA assets, Utility grid assets, and enterprise IT assets in commercial datacenters. 

Naval Prognostic Applications for MSET2 Algorithm

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### Welcome to the 7th annual workshop on Naval Applications of Machine Learning!

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

**General Chairs:**

Katie Rainey, Naval Information Warfare Center Pacific

Nicole Isoda, Naval Information Warfare Center Pacific<br><br>

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NAML 2023

The 7th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


Kenny Gross

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Naval Prognostic Applications for MSET2 Algorithm

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