DocumentCode :
671616
Title :
Optimized neuro genetic fast estimator (ONGFE) for efficient distributed intelligence instantiation within embedded systems
Author :
Maldonado, Francisco J. ; Oonk, Stephen ; Politopoulos, Tasso
Author_Institution :
American GNC Corp., Simi Valley, CA, USA
fYear :
2013
fDate :
4-9 Aug. 2013
Firstpage :
1
Lastpage :
8
Abstract :
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 intelligence instantiation within embedded systems. The original design had the purpose of providing a real time kernel to deploy health monitoring functions for Condition Based Maintenance (CBM) and Real Time Monitoring (RTM) systems in a broad variety of applications (such as aerospace, structural, and widely distributed support systems). The ONGFE contains embedded fast and on-line training for designing ANNs to perform several high performance FDI&P functions. A key advantage of this technology is an optimization block based upon pseudogenetic algorithms which compensate for effects due to initial weight values and local minimums without the computational burden of genetic algorithms. The ONGFE also provides a synchronization block for communication with secondary diagnostic modules. The algorithms are designed for a distributed, scalar, and modular deployment. Based on this technology, a scheme for conducting sensor data validation has been embedded in Smart Sensors.
Keywords :
application program interfaces; condition monitoring; distributed processing; embedded systems; failure analysis; fault diagnosis; genetic algorithms; intelligent sensors; learning (artificial intelligence); maintenance engineering; neural nets; structural engineering computing; synchronisation; ANN design; API; CBM; FDIP capability; FDIP functions; ISE; ONGFE; RTM systems; application programming interface; artificial neural networks; component failure detection-identification-prognostic capability; condition based maintenance; distributed deployment; distributed intelligence instantiation; embedded online training; embedded systems; embedding subsystem; health monitoring functions; intelligent software elements; modular deployment; optimization block; optimized neuro genetic fast estimator; pseudogenetic algorithms; real time kernel; real time monitoring systems; scalar deployment; secondary diagnostic modules; sensor data validation; smart sensors; software tool; Algorithm design and analysis; Artificial intelligence; Artificial neural networks; Computer architecture; Monitoring; Software; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location :
Dallas, TX
ISSN :
2161-4393
Print_ISBN :
978-1-4673-6128-6
Type :
conf
DOI :
10.1109/IJCNN.2013.6706957
Filename :
6706957
Link To Document :
بازگشت