Abstract: The architecture design and functional operation of an enterprise data processing system with distributed intelligence capable of monitoring sensor networked systems and processing large sets of data as required by the application is disclosed in this invention. The system is based on smart sensor/actuator nodes, local processing units, a server, and clients. Smart nodes interface with transducers for conducting data acquisition or control and consist of a small processor, a communications module, and transducer interfacing circuitry. The processor includes instructions to execute commands and may contain small-form factor processing algorithms. Sensor and processed data is then transmitted to the local processing units which perform higher level processing and contain functions for managing the nodes. The local processing units have small databases that are updated with the status of the monitored system. These devices then communicate with a server based on commands and automated event messages.

Abstract: A Coremicro Reconfigurable Embedded Smart Sensor Node has the capability of hosting intelligent algorithms to support health monitoring applications and has optional standardized software communications stack. The purpose of this present invention is to provide a flexible low power distributed computational platform to deploy intelligent software elements (based on Artificial Intelligence techniques) among the system architecture to result in a reconfigurable scheme for distributed intelligence granularity. This invention is able to be applied to a wide variety of monitoring applications either as a Standalone Smart Sensor (SSS, i.e. single Smart Sensor Node) or as a modular and scalable Smart Sensor Network configuration. Therefore, the CRE-SSN is ultra-low in power consumption, has optional pattern recognition through Artificial Neural Network, physical communication layer reconfigurable capability, has scalable communications capability, and low in weight, and optimized in size.

Abstract: A real time kernel for deploying health monitoring functions in Condition Base Maintenance (CBM) and Real Time Monitoring (RTM) systems is disclosed in this invention. The Optimized Neuro Genetic Fast Estimator (ONGFE) allows embedding failure detection, identification, and prognostics (FDI&P) capability by using Intelligent Software Element (ISE) based upon Artificial Neural Network (ANN). ONGFE enables embedded fast and on-line training for designing ANNs, which perform very high performance FDI&P functions. An advantage is the optimization block based on pseudogenetic algorithms, which compensate for effects due to initial weight values and local minimums without the computational burden of genetic algorithms. It provides a synchronization block for communication with secondary diagnostic modules. Also a scheme for conducting sensor data validation is embedded in Smart Sensors (SS). The algorithms are designed for a distributed, scalar, and modular deployment.

Abstract: A real time kernel for deploying health monitoring functions in Condition Base Maintenance (CBM) and Real Time Monitoring (RTM) systems is disclosed in this invention. The Optimized Neuro Genetic Fast Estimator (ONGFE) allows embedding failure detection, identification, and prognostics (FDI&P) capability by using Intelligent Software Element (ISE) based upon Artificial Neural Network (ANN). ONGFE enables embedded fast and on-line training for designing ANNs, which perform very high performance FDI&P functions. An advantage is the optimization block based on pseudogenetic algorithms, which compensate for effects due to initial weight values and local minimums without the computational burden of genetic algorithms. It provides a synchronization block for communication with secondary diagnostic modules. Also a scheme for conducting sensor data validation is embedded in Smart Sensors (SS). The algorithms are designed for a distributed, scalar, and modular deployment.