ETV is an Economically Disadvantaged Woman-Owned small business (recertification in process) and HUBZone Certified Small Business based in Alamogordo since 2014. ETV develops integrated, cross-domain autonomous system solutions with artificial intelligence/machine learning driven analytics in military and commercial mission scenarios. KeenAI represents the next step in the team’s vision to advance autonomous sensing and predictive analytics in complex environments to meet market opportunities in critical infrastructure inspection and maintenance (aerospace, building information management, renewable energy), precision agriculture, defense, and public safety.
KeenAI–Autonomous Inspection of Large Marine Shafts
The Autonomous Inspection of Large Marine Shafts solution is adapted and scaled for use by Navy shipyards to allow autonomous inspection, anomaly detection and characterization, as well as post-repair inspection of large marine shafts. The solution uses a gantry-style inspection approach to support the ability to inspect marine shafts placed on power rollers currently used in the maintenance workflow. The autonomous inspection workflow adapts to each shaft as necessary to ensure full coverage of the marine shaft including shaft ends, O-rings, and flanges that may be present at the stage of disassembly for the article under inspection. This approach, along with the KeenAI neural network engine and digital twin strategy, permits success of full shaft inspection and analysis within an objective key performance parameter of an eight-hour shift.
- Large ship overhauls conducted at Navy shipyards involve extensive processes, many of which are performed manually, extending repair time and cost.
- Marine shafts between 25-75 feet long can take weeks to inspect, to identify and document defects for subsequent repair.
Technology Solution Statement:
KeenAI’s gantry design ensures complete shaft coverage, including shaft ends, O-rings, and flanges that may be present. This approach, along with KeenAI’s neural network engine and digital twin strategy, permits successful full-shaft inspection and analysis within an objective key performance parameter of an eight-hour shift. The open architecture supports additional subsystems as required for future growth.
- Builds 3D digital twin from as built drawings
- Utilizes existing infrastructure platforms w/automated gantry (rail or overhead) for scanning
- Enables moving between workstations for optimization
- Includes integrated platform controller for rotation
- Can preprogram inspection sequence by shaft serial number
- Utilizes existing inspection workflow to facilitate transition between autonomous and manual
- Uses camera/laser scanner photogrammetry to update 3D digital twin with anomalies creating 4D digital twin (4th D = Time)
- Allows layered visualization to view previous anomalies/repairs
Eugene C. “Cliff” Hudson