Shane Strasser

Graph-based ontology-guided data mining for D-matrix model maturation

In model-based diagnostic algorithms, it is assumed that the model is correct. If the model is incorrect, the diagnostic algorithm may diagnose the wrong fault, which can be costly and time consuming. Using past maintenance events, one should be able to make corrections to the model in order for diagnostic algorithm to correctly diagnosis faults. In this paper, a maturation approach is proposed which uses the graph-theoretic representations of Timed Failure Propagation Graph (TFPG) models and diagnostic sessions based on recently standardized diagnostic ontologies to determine statistical discrepancies between that which is expected by the models and that which has been encountered in practice. These discrepancies are then analyzed to generate recommendations for maturing the diagnostic models. Maturation recommendations include identifying new dependencies and erroneous or tenuous dependencies. 1 2

Factored Evolutionary Algorithms

Factored evolutionary algorithms (FEAs) are a new class of evolutionary search-based optimization algorithms that have successfully been applied to various problems, such as training neural networks and performing abductive inference in graphical models. An FEA is unique in that it factors the objective function by creating overlapping subpopulations that optimize over a subset of variables of the function. In this paper, we give a formal definition of FEA algorithms and present empirical results related to their performance. One consideration in using an FEA is determining the appropriate factor architecture, which determines the set of variables each factor will optimize. For this reason, we present the results of experiments comparing the performance of different factor architectures on several standard applications for evolutionary algorithms. Additionally, we show that FEA’s performance is not restricted by the underlying optimization algorithm by creating FEA versions of hill climbing, particle swarm optimization, genetic algorithm, and differential evolution and comparing their performance to their single-population and cooperative coevolutionary counterparts.

Diagnostic alarm sequence maturation in timed failure propagation graphs

Diagnostic model development presents a significant engineering challenge to ensure subsequent diagnostic processes using such models will yield accurate results. One approach to developing system-level diagnostic models that has been receiving attention is the Timed Failure Propagation Graph (TFPG), developed at Vanderbilt University. Unfortunately, developing TFPG models is also difficult and error-prone. In this paper, we extend previous work in using historical maintenance and diagnostic information to identify potential errors in the TFPG-based diagnostic models and recommend ways of maturing these models. This is done by extending the maturation process to incorporate historical alarm sequences and to model these sequences using a probabilistic transition matrix (similar to a Markov chain). The resulting sequence model is compared to the causal relationships identified in the original TFPG to discover discrepancies between the two. Potential sequence modeling errors with recommendations are given back to an engineer or analyst. We report on the maturation process and algorithms and also provide preliminary experimental results.

Abductive inference in Bayesian networks using distributed overlapping swarm intelligence

In this paper we propose several approximation algorithms for the problems of full and partial abductive inference in Bayesian belief networks. Full abductive inference is the problem of finding the

A New Discrete Particle Swarm Optimization Algorithm

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Learning Bayesian classifiers using overlapping swarm intelligence

k most probable state assignments to all non-evidence variables in the network while partial abductive inference is the problem of finding the

Relaxing Consensus in Distributed Factored Evolutionary Algorithms

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