Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Engineering
A Framework For Systematic Benchmarking Of Monitoring And Diagnostic Systems, Tolga Kurtoglu, Ole J. Mengshoel, Scott Poll
A Framework For Systematic Benchmarking Of Monitoring And Diagnostic Systems, Tolga Kurtoglu, Ole J. Mengshoel, Scott Poll
Ole J Mengshoel
In this paper, we present an architecture and a formal framework to be used for systematic benchmarking of monitoring and diagnostic systems and for producing comparable performance assessments of different diagnostic technologies. The framework defines a number of standardized specifications, which include a fault catalog, a library of modular test scenarios, and a common protocol for gathering and processing diagnostic data. At the center of the framework are 13 benchmarking metric definitions. The calculation of metrics is illustrated on a probabilistic model-based diagnosis algorithms utilizing Bayesian reasoning techniques. The diagnosed system is a real-world electrical power system, namely the Advanced …
Using Bayesian Networks For Candidate Generation In Consistency-Based Diagnosis, Sriram Narasimhan, Ole J. Mengshoel
Using Bayesian Networks For Candidate Generation In Consistency-Based Diagnosis, Sriram Narasimhan, Ole J. Mengshoel
Ole J Mengshoel
Consistency-based diagnosis relies on the computation of discrepancies between model predictions and sensor observations. The traditional assumption that these discrepancies can be detected accurately (by means of thresholding for example) is in many cases reasonable and leads to strong performance. However, in situations of substantial uncertainty (due, for example, to sensor noise or model abstraction), more robust schemes need to be designed to make a binary decision on whether predictions are consistent with observations or not. However, if an accurate binary decision is not made, there are risks of occurrence of false alarms and missed alarms. Moreover when multiple sensors …
Understanding The Role Of Noise In Stochastic Local Search: Analysis And Experiments, Ole J. Mengshoel
Understanding The Role Of Noise In Stochastic Local Search: Analysis And Experiments, Ole J. Mengshoel
Ole J Mengshoel
Stochastic local search (SLS) algorithms have recently been proven to be among the best approaches to solving computationally hard problems. SLS algorithms typically have a number of parameters, optimized empirically, that characterize and determine their performance. In this article, we focus on the noise parameter. The theoretical foundation of SLS, including an understanding of how to the optimal noise varies with problem difficulty, is lagging compared to the strong empirical results obtained using these algorithms. A purely empirical approach to understanding and optimizing SLS noise, as problem instances vary, can be very computationally intensive. To complement existing experimental results, we …
Sensor Validation Using Bayesian Networks, Ole J. Mengshoel, Adnan Darwiche, Serdar Uckun
Sensor Validation Using Bayesian Networks, Ole J. Mengshoel, Adnan Darwiche, Serdar Uckun
Ole J Mengshoel
One of NASA’s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation techniques address this problem: given a vector of sensor readings, decide whether sensors have failed, therefore producing bad data. We take in this paper a probabilistic approach, using Bayesian networks, to diagnosis and sensor validation, and investigate several relevant but slightly different Bayesian network queries. We emphasize that onboard inference can be performed on a …
Diagnosing Faults In Electrical Power Systems Of Spacecraft And Aircraft, Ole J. Mengshoel, Adnan Darwichse, Keith Cascio, Mark Chavira, Scott Poll, Serdar Uckun
Diagnosing Faults In Electrical Power Systems Of Spacecraft And Aircraft, Ole J. Mengshoel, Adnan Darwichse, Keith Cascio, Mark Chavira, Scott Poll, Serdar Uckun
Ole J Mengshoel
Electrical power systems play a critical role in spacecraft and aircraft. This paper discusses our development of a diagnostic capability for an electrical power system testbed, ADAPT, using probalistic techniques. In the context of ADAPT, we present two challenges, regarding modelling and real-time performance, often encountered in real-world diagnostic applications. To meet the modelling challenge, we discuss our novel high-level specification language which supports auto-generation of Bayesian networks. To meet the real-time challenge, we compile Bayesian networks intro arithmetic circuits. Arithmetic circuits typically have small footprints and are optimized for the real-time avionics systems found in spacecraft and aircraft. Using …