Engineering Commons^™

A Tutorial On Bayesian Networks For System Health Management, Arthur Choi, Lu Zheng, Adnan Darwiche, Ole J. Mengshoel

Ole J Mengshoel

Bayesian networks have established themselves as an indispensable tool in artificial intelligence, and are being used effectively by researchers and practitioners more broadly in science and engineering. The domain of system health management, including diagnosis, is no exception. In fact, diagnostic applications have driven much of the developments in Bayesian networks over the past few decades. In this chapter, we provide a gentle and accessible introduction to modeling and reasoning with Bayesian networks, with the domain of system health management in mind.

Bayesian Software Health Management For Aircraft Guidance, Navigation, And Control, Johann M. Schumann, Timmy Mbaya, Ole J. Mengshoel

Ole J Mengshoel

Modern aircraft — both piloted fly-by-wire commercial aircraft as well as UAVs — more and more depend on highly complex safety critical software systems with many sensors and computer-controlled actuators. Despite careful design and V&V of the software, severe incidents have happened due to malfunctioning software.

In this paper, we discuss the use of Bayesian networks to monitor the health of the on-board software and sensor system, and to perform advanced on-board diagnostic reasoning. We focus on the development of reliable and robust health models for combined software and sensor systems, with application to guidance, navigation, and control (GN&C). Our …

Integrating Probabilistic Reasoning And Statistical Quality Control Techniques For Fault Diagnosis In Hybrid Domains, Brian Ricks, Craig Harrison, Ole J. Mengshoel

Ole J Mengshoel

Visualizing And Understanding Large-Scale Bayesian Networks, Michele Cossalter, Ole J. Mengshoel, Ted Selker

Ole J Mengshoel

Bayesian networks are a theoretically well-founded approach to represent large multi-variate probability distributions, and have proven useful in a broad range of applications. While several software tools for visualizing and editing Bayesian networks exist, they have important weaknesses when it comes to enabling users to clearly understand and compare conditional probability tables in the context of network topology, especially in large-scale networks. This paper describes a system for improving the ability for computers to work with people to develop intelligent systems through the construction of high-performing Bayesian networks. We describe NetEx, a tool developed as a Cytoscape plugin, which allows …

Software Health Management With Bayesian Networks, Ole J. Mengshoel, Johann M. Schumann

Ole J Mengshoel

No abstract provided.

Integrated Software And Sensor Health Management For Small Spacecraft, Johann Schumann, Ole J. Mengshoel, Timmy Mbaya

Ole J Mengshoel

Despite their size, small spacecraft have highly complex architectures with many sensors and computer-controlled actuators. At the same time, size, weight, and budget constraints often dictate that small spacecraft are designed as single-string systems, which means that there are no or few redundant systems. Thus, all components, including software, must operate as reliably. Faults, if present, must be detected as early as possible to enable (usually limited) forms of mitigation. Telemetry bandwidth for such spacecraft is usually very limited. Therefore, fault detection and diagnosis must be performed on-board. Further restrictions include low computational power and small memory.

In this paper, …

Belief Propagation By Message Passing In Junction Trees: Computing Each Message Faster Using Gpu Parallelization, Lu Zheng, Ole J. Mengshoel, Jike Chong

Ole J Mengshoel

Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among the most prominent approaches to computing posteriors in BNs. However, belief propagation over junction tree is known to be computationally intensive in the general case. Its complexity may increase dramatically with the connectivity and state space cardinality of Bayesian network nodes. In this paper, we address this computational challenge using GPU parallelization. We develop data structures and algorithms that extend existing junction tree techniques, and specifically develop a novel approach to computing each belief propagation message in parallel. We implement our approach on an NVIDIA …

Verification And Validation Of System Health Management Models Using Parametric Testing, Erik Reed, Johann Schumann, Ole J. Mengshoel

Ole J Mengshoel

System Health Management (SHM) systems have found their way into many safety-critical aerospace and industrial applications. A SHM system processes readings from sensors throughout the system and uses a Health Management (HM) model to detect and identify potential faults (diagnosis) and to predict possible failures in the near future (prognosis). It is essential that a SHM system, which monitors a safety-critical component, must be at least as reliable and safe as the component itself—false alarms or missed adverse events can potentially result in catastrophic failures. The SHM system including the HM model, a piece of software, must therefore undergo rigorous …

Initialization And Restart In Stochastic Local Search: Computing A Most Probable Explanation In Bayesian Networks, Ole J. Mengshoel, David C. Wilkins, Dan Roth

Ole J Mengshoel

For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which algorithms are effective for initialization? When should the search process be restarted? In the present work, we investigate these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary …