\n 04.17.2026\u00a0\u00a0\u2013\u00a0\u00a0
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In a soundproof chamber at the Naval Surface Warfare Center, Philadelphia Division (NSWCPD), a steel-gray air compressor hums as engineers test an experimental machine learning model designed to detect early signs of failure through vibration analysis. The effort, part of NSWCPD\u2019s growing portfolio of artificial intelligence (AI) and machine learning (ML) projects, shows early promise for potential future Navy use.<\/p>\n
High-pressure air compressors play an important role in submarine operations by supporting several key ship functions. Their reliable performance helps crews maintain maneuverability and keep the mission on track.<\/p>\n
The Navy\u2019s Condition-Based Maintenance Plus (CBM+) initiative combines traditional maintenance practices with artificial-intelligence-driven prognostics, aiming not only to detect a problem but also, over time, to estimate how far it has progressed and how long until equipment fails. At NSWCPD, the compressor project is an exploratory step in that direction \u2014 a way to see what AI and ML can do with carefully collected vibration data before the Navy considers operational use.<\/p>\n
\u201cAs a warfare center, we are performing applied research into how AI and machine learning can sharpen the tools we provide our Sailors,\u201d NSWCPD Technical Director Nigel C. Thijs, SES, said. \u201cProjects like this help us understand where AI adds value, where it still falls short, and how we can align digital innovation with our core mission of delivering warfighting capability in both acquisition and sustainment to the fleet.\u201d<\/p>\n
NSWCPD has been building AI\/ML expertise across multiple fronts. Engineer Dr. Kaitlyn Sitch recently earned a Science, Mathematics, and Research for Transformation (SMART) SEED Innovation Award for developing AI\/ML algorithms to predict power-system health, an effort aimed at reducing downtime and improving energy resilience across Navy platforms. The command is also advancing \u201cdigital twins\u201d for shipboard systems, such as Enterprise Remote Monitoring (eRM), using Python-based machine learning models to forecast anomalies in hull, mechanical, and electrical equipment. It previously hosted a Navy-wide prize challenge that drew industry prototypes for AI-enabled information-marking tools. Together, these initiatives frame the compressor work as part of a broader push to apply data science to real-world Navy problems, not as a stand-alone solution.<\/p>\n
Within that larger ecosystem, the current compressor model is intentionally constrained. Because real-world failures on operational platforms are rare \u2014 a positive outcome for Sailors but a challenge for data science \u2014 NSWCPD engineers built a dedicated test loop and induced faults, including simulated air leaks, inlet restrictions, and cooling-water problems, under controlled conditions. They used arrays of accelerometers to capture every change in vibration. The resulting data provided the team with a safe starting point to assess how different machine learning approaches distinguish healthy behavior from known faults.<\/p>\n
\u201cOur lab tests to date show real promise: on sample data, our machine learning models distill thousands of vibration features into just 10 key indicators that reliably flag common faults, such as leaks and restrictions,\u201d said NSWCPD Machine Learning Engineer Colin Dingley, also a Certified Information Systems Security Professional in the command\u2019s cybersecurity workforce. \u201cThe next step is to scale AI with more diverse data and edge hardware to see whether this holds up in real-world conditions \u2014 it\u2019s challenging, but the early results are encouraging.\u201d<\/p>\n
Submarines add a distinctive constraint: limited underwater bandwidth prevents sending full-fidelity vibration streams ashore for analysis. That reality shifts processing to \u201cthe edge\u201d\u2014compact, energy-efficient hardware mounted near the machinery that can run AI models locally and share only the most important health information with operators and shore systems. Luna Labs designed the embedded eCBM node and edge device that NSWCPD is testing in the Anechoic Chamber, a quiet environment where engineers can evaluate how well the algorithms perform without interference from other shipboard noise.<\/p>\n
\u201cAI will complement our Sailors,\u201d Dingley said. \u201cMachine learning and artificial intelligence will become part of a Sailor\u2019s tool belt \u2014 another layer of protection. It\u2019s been said that a Sailor has 26 hours of maintenance in a 24-hour day. We\u2019re trying to make that more manageable by using AI to highlight which components truly need their attention.\u201d<\/p>\n
NSWCPD CBM+\/Prognostic Health Monitoring Lead Sherwood \u201cWoody\u201d Polter traces this line of research to 2012, long before AI became a household word and before today\u2019s processors could run large models at the edge.<\/p>\n
\u201cAs a Navy laboratory, we routinely partner with industry, academia, and other government organizations to advance technology,\u201d Polter said. \u201cThanks to computers capable of processing these large models, we can experiment with AI-enabled health monitoring in ways that simply were not feasible a decade ago.\u201d<\/p>\n
Polter emphasizes that no single machine learning model can handle every shipboard system. Each piece of equipment has its own dynamics, operating ranges, and failure modes, but the high-pressure air compressor used in this project is common across the surface and undersea fleets, making it an ideal testbed.<\/p>\n
\u201cThis type of compressor is found throughout the fleet,\u201d he said. \u201cIt is critical on submarines, but that is just the tip of the iceberg.\u201d<\/p>\n
The long-term vision extends beyond manned platforms.<\/p>\n
\u201cBesides surface ships and submarines, we are actively pursuing machine learning with eCBM technology for unmanned undersea platforms,\u201d Polter said. \u201cOn those systems, where no crew is standing nearby with a wrench and a clipboard, trusted autonomy will depend as much on reliable self-monitoring as on navigation and communications.\u201d<\/p>\n
At its core, Polter describes the work as \u201ccondition-based monitoring plus prognostics.\u201d He often uses a car example: the owner\u2019s manual may recommend changing a fan belt at a set mileage, but an AI-enabled vehicle might one day tell the driver that, given how that belt was installed and used, it is likely to fail within 30 days.<\/p>\n
\u201cApply that to being on a ship,\u201d he said. \u201cBefore something breaks, it bends. Our goal is to find that bend in the data.\u201d<\/p>\n
In practice, \u201cfinding the bend\u201d means focusing on vibration as a rich source of information.<\/p>\n
\u201cWe primarily use vibration sensor data and can detect outlier frequencies of interest, which we compare to baseline measurements,\u201d Polter said. \u201cThat enables us to apply data analytics and our algorithms to build machine learning models for anomaly detection.\u201d<\/p>\n
On an ordinary day, this looks more like pattern recognition than drama: spectra shifting slightly away from normal, and algorithms quietly flagging patterns a human might never see.<\/p>\n
Looking ahead, the team views the estimation of remaining useful life (RUL) as a longer-term goal rather than an immediate deliverable.<\/p>\n
\u201cRemaining useful life analyzes a lot of data,\u201d Polter explained. \u201cThe operator will be informed of the specific part or component within the system that is expected to fail. There will be requirements for the system to notify the user or, if it\u2019s an unmanned system, to send remote data to the platform operator. But all of this is only possible if the models are well developed, using big data, to provide an RUL solution.\u201d<\/p>\n
For now, NSWCPD leaders view the compressor project as one of several \u201clearning labs\u201d for AI\/ML within the command.<\/p>\n
\u201cThis work will directly impact the warfighter,\u201d Thijs said. \u201cWhen we transition AI\u2011enabled health monitoring from the lab to the fleet, as we are already doing with condition\u2011based monitoring and enterprise Remote Monitoring, we can help Sailors better prepare for or avoid casualties, increase operational time, and derive more value from every maintenance dollar.\u201d<\/p>\n
\u201cThis technology is not limited to submarines,\u201d Dingley added. \u201cThe umbrella is much broader than just a compressor. Theoretically, it can work on any device \u2014 if we do the hard work up front to understand the data and train the models properly.\u201d<\/p>\n
As the compressor wound down in the testing facility after its latest run, the sound faded into silence, but the data it produced did not. Those records now feed a growing digital ecosystem of models, simulations, and lessons learned, shaping how NSWCPD will bring AI and machine learning to future machinery health projects.<\/p>\n
\u201cEvery time we improve the Navy\u2019s ability to see a problem before it becomes dangerous, we\u2019re protecting our Sailors,\u201d Dingley said. \u201cThat\u2019s what this is really about.\u201d<\/p>\n
NSWCPD employs approximately 2,700 civilian engineers, scientists, technicians, and support personnel. The command conducts research and development, test and evaluation, acquisition support, and in-service and logistics engineering for non-nuclear machinery, ship machinery systems, and related equipment and material for Navy surface ships and submarines, and serves as the lead organization providing cybersecurity for all ship systems.<\/p>\n","protected":false},"excerpt":{"rendered":" 04.17.2026\u00a0\u00a0\u2013\u00a0\u00a0 In a soundproof chamber at the Naval Surface Warfare Center, Philadelphia Division (NSWCPD), a steel-gray air…\n","protected":false},"author":2,"featured_media":7135,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[6438,24,25,372,389,6422,6423,6424,6429,6432,6427,6428,6442,6441,6440,6437,6436,6435,6434,6439,6426,6425,6430,6433,6431],"class_list":{"0":"post-7134","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai","8":"tag-aegis","9":"tag-ai","10":"tag-artificial-intelligence","11":"tag-engineering","12":"tag-naval","13":"tag-naval-sea-systems-command","14":"tag-navsea","15":"tag-navy","16":"tag-navy-command","17":"tag-navy-jobs","18":"tag-navy-systems","19":"tag-navy-systems-command","20":"tag-nnptc","21":"tag-nossa","22":"tag-nptu","23":"tag-rmc","24":"tag-shipyards","25":"tag-supervisor-of-salvage-and-diving","26":"tag-supship","27":"tag-training-command","28":"tag-u-s-navy","29":"tag-us-navy","30":"tag-veteran-jobs","31":"tag-warfare-centers","32":"tag-wounded-warrior"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/7134","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=7134"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/7134\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/7135"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=7134"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=7134"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=7134"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}