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With acoustic-electric channels, it is possible to send data through metallic barriers at very high rates while also transferring power. However, some isolated sensing situations require a lower complexity solution that is both small in size and low-power. This paper describes a simple low-rate communication method optimized for use with a battery-powered wireless sensor using amplitude modulation,...
Obtaining methodologies that enable predictive health monitoring of components degradation and the propagation of related effects across the overall system is a need when designing complex systems (such as autonomous vehicles, robotic systems, and aerospace platforms). In this paper, a current software development is presented for workflow generation and visualization to evaluate how component degradation...
Large and complex systems such as space vehicles, power plants, manufacturing facilities, oil refineries, gas delivery systems, among others often have networks of alarms monitoring basic parameters (e.g. high or low temperature, voltage out-of-tolerance, power loss, etc.) which are correlated to failure modes, but not necessarily in a very direct way. In this paper, we present a plurality of graph-based...
Embedded self-learning is a desired capability that can enhance autonomy in different types of unmanned systems. Autonomous diagnostics is an area of opportunity to deploy this capability, which allows for vehicle failure awareness and enables for other advantageous schemes such as fault tolerant control. In this paper, we present one subsystem of an ensemble of schemes that form the Enhanced Autonomous...
Most unmanned mobile robotic platforms contain multiple sensors that can be leveraged to measure vehicle motion states, where there often exists redundancies among the different sensor types. Kalman filter based sensor fusion between inertial navigation sensors, GPS readings, encoders, etc. is a very popular approach in the literature to improve the accuracy of navigation readings. However, such redundancies...
The Optimized Neuro Genetic Fast Estimator (ONGFE) is a software tool that allows for embedding system, subsystem, and component failure detection, identification, and prognostics (FDI&P) capability by using Intelligent Software Elements (ISE) based upon Artificial Neural Networks (ANN). With an Application Programming Interface (API), highly innovative algorithms are compiled for efficient distributed...
This paper provides an overview of a novel scheme for constructing machine evolutionary behavior within systems. Specifically, evolving learning for the autonomous recognition of both known as well as newly emerging behaviors is provided. The paper is related with several open research problems such as cognition, incremental learning, and self-learning within the context of health monitoring systems...
The ability to predict and understand the responses of an airframe with simultaneous external influences acting on its elements is a challenging effort. Moreover, although considerable research has been devoted to monitoring structures within the aerospace industry (commercial, military, and space), successful field implementations have not been widely achieved. Breakthroughs for in-flight measurement...
This paper discusses a Structural Health Monitoring framework developed for aircraft airframes, where the objective is high performance vibration-based diagnostics using validated data from low power and miniaturized smart sensors. Although considerable research has been devoted to the structural health monitoring discipline, successful field implementations have not been widely achieved. This research...
Condition monitoring systems capable of efficiently and accurately diagnosing and identifying faults is a current need for ensuring the proper operation of critical systems. Distributed health monitoring leveraging large sensor networks that provide validated data ensures the proper operation and performance of systems. A key consideration is to have non-intrusive embedded sensors that can be easily...
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