Some of the primary GTC developed technologies/products are:
RaNDL – RApid Nuclear & seismic events Discriminator & Locator using AI
RaNDL uses deep neural networks trained on tens of thousands of past seismic events (nuclear blasts, earthquakes, chemical blasts, etc.) to discriminate between the type of events. RaNDL will also be able to associate signals coming in from different geographical locations to ascertain the origin of the seismic waves. This tool will enable automated and robust seismic signal processing, event classification, and location identification of different seismic events. The deep neural networks used will also lower the threshold of detection magnitude which will be useful to monitor fracking, waster water injection, mining activities for induced micro-earthquakes which are precursors for larger adverse events. Managing and modifying activities will prevent huge losses in these industries.
Corrosion Modeling & Life Cycle Analysis of Metal Canisters – CaskADES
CaskADES is a physics-based life cycle analysis modeling tool for steel canisters subject chloride-induced stress corrosion cracking (CISCC). The tool applies environmental conditions based on input locations & site data and updates canister state based on visual inspections and any crack mitigation operations performed like cold spraying, shot peening, and laser peening operations. CaskADES is designed for the management of spent nuclear fuel holding steel canisters, license extension applications, but is applicable for all steel canisters undergoing CISCC.
Automated Contingency Management of Water Recycling Systems
GTC has developed software for modeling and automated control of water recycling systems and its subcomponents. The software includes modeling of primary failure mechanisms: filter clogging and pump failures. Algorithms detect incipient failures and apply automated advanced controls (PID + Model Predictive Control) to optimize operations using a multi-objective cost function. GTC has deployed this technology with a cloud-based approach that ties into legacy PLC systems for remote system control, data management, and predictive maintenance and warning system.
The origins of this technology are for NASA’s Space Habitats where processing power is limited; thus, the framework embeds event-based sampling to minimize the required CPU processing power. The software can be used for condition-based maintenance, remaining useful life predictions, and to satisfy strict operational and mission constraints, especially for space and remote applications.
Automated Resource Management Strategies for Resilient Habitat Systems
GTC has developed a framework utilizing machine learning algorithms to maintain the resilience of space habitat life support systems under subsystem failure conditions. In space and submarine operations, it becomes very critical to manage habitat conditions and resources like O2, N2, CO2 levels, drinking water, etc. to successfully complete missions. When a component or subsystem is compromised, the optimized control settings and schedule of operation of different systems on the habitat is not trivial. This framework allows machine learning algorithms to automate system control from data generated from a large number of previous simulations and optimizations which are not possible to implement in real-time during a mission due to computational resource constraints. This framework is applicable to complex systems comprised of many subsystems that operate together and interact with each other for resources like submarines, space habitats, ships, etc.
Hierarchical Fault Tolerant Control System for Unmanned Ground Vehicles (UGVs)
Global Technology Connection, Inc. developed rollover prediction/prevention and battery life monitoring algorithms for PackBots. Prevention of rollover in packBots is critical as loss of robotic platforms can lead to failure of missions for soldiers on the field. Likewise, battery life monitoring is also important to missions where accurate state-of-charge and battery health aids in mission planning. Data collected from experiments on testbeds were used to design a fuzzy logic rollover risk assessment algorithm that provides normal, caution, and critical level notifications to the operator. When automatic rollover prevention is enabled, the robot will apply a brake when rollover risk is critical. The software package is ready for field deployment and experiments as an add-on package for robotic platforms.
Aircraft Generator Diagnostics and Health Management Tool
Global Technology Connection, Inc. (GTC) has developed aircraft generator diagnostic algorithms to differentiate between failure modes (bearings, winding failures, etc.), normal aircraft operational modes (speed, load, etc.), and environments. The algorithms correlate electrical signatures to the state-of-health (failure type, severity, and location) of low-hour/healthy and degraded generators. The algorithms are embedded into a walkup tester (portable PC, data acquisition unit, sensors) and an aircraft power generation system to manage the health of the system. The walkup tester can be used on generators during maintenance or pre/post-flight checks of generators onboard aircraft on the ground.
Dynamic Maintenance Scheduling Algorithm for Military Aircraft Fleet
GTC developed a framework to utilize prognostic health information from aircraft systems like fuel pumps to improve aircraft fleet maintenance schedules. The algorithms consider resource (parts, depot availability, tools) and personnel availability constraints and rules for maintenance operations (order of operations). The framework also allows for unscheduled maintenance operations to be dynamically included to minimize overall delay and schedule related costs. In order to achieve an optimal schedule solution, costs are developed for delays in maintenance, delays or unavailability of aircraft for a mission, etc, and the schedule with the lowest cost is arrived at using automated optimization algorithms without requiring any human effort.
Valve Prognostic Health Monitoring System
Global Technology Connection, Inc. in collaboration with academic partners developed a Valve Prognostic Health Monitoring System (VPHMS) that detects and identifies valve incipient failure modes and estimates valve remaining useful life from sensor data. Hydraulically actuated butterfly valves are considered as they are commonly used in the industry. Valve failure modes that are primarily examined are seal failures, sensor failures, loose linkages, worn bearings, and degraded/contaminated hydraulic fluids. Valve failures will be identified through a set of condition indicator features and a fuzzy logic expert rule-base. The prognostics approach utilizes a trended anomaly detection output along with remaining useful life (RUL) threshold governed by the identified failure to predict the time-to-maintenance for a valve