Engineering Commons^™

Multi-Focus And Multi-Window Techniques For Interactive Network Exploration, Priya K. Sundararajan, Ole J. Mengshoel, Ted Selker

Ole J Mengshoel

Multi-Focus And Multi-Level Techniques For Visualization And Analysis Of Networks With Thematic Data, Michele Cossalter, Ole J. Mengshoel, Ted Selker

Ole J Mengshoel

Reactive Bayesian Network Computation Using Feedback Control: An Empirical Study, Ole J. Mengshoel, Abe Ishihara, Erik Reed

Ole J Mengshoel

Adaptive Control Of Bayesian Network Computation, Erik Reed, Abe Ishihara, Ole J. Mengshoel

Ole J Mengshoel

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.

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 …

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 …

Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study, Ole J. Mengshoel, Mark Chavira, Keith Cascio, Adnan Darwiche, Scott Poll, Serdar Uckun

Ole J Mengshoel

We present in this paper a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system (EPS), i.e., the Advanced Diagnstic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. Our probabilistic approach is formally well founded and based on Bayesian networks (BNs) and arithmetic circuits (ACs). We pay special attention to meeting two of the main challenges often associated with real-world application of model-based diagnosis technologies: model development and real-time reasoning. To address the challenge of model development, we develop a systematic approach to representing EPSs as BNs, …

Diagnosing Intermittent And Persistent Faults Using Static Bayesian Networks, Ole J. Mengshoel, Brian Ricks

Ole J Mengshoel

Both intermittent and persistent faults may occur in a wide range of systems. We present in this paper the introduction of intermittent fault handling techniques into ProDiagnose, an algorithm that previously only handled persistent faults. We discuss novel algorithmic techniques as well as how our static Bayesian networks help diagnose, in an integrated manner, a range of intermittent and persistent faults. Through experiments with data from the ADAPT electrical power system test bed, generated as part of the Second International Diagnostic Competition (DXC-10), we show that this novel variant of ProDiagnose diagnoses intermittent faults accurately and quickly, while maintaining strong …

Developing Large-Scale Bayesian Networks By Composition: Fault Diagnosis Of Electrical Power Systems In Aircraft And Spacecraft, Ole J. Mengshoel, Scott Poll, Tolga Kurtoglu

Ole J Mengshoel

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifiations, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability …

Diagnosis And Reconfiguration Using Bayesian Networks: An Electrical Power System Case Study, W. Bradley Knox, Ole J. Mengshoel

Ole J Mengshoel

Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task.

More specifically, we discuss the auto-generation of …

The Diagnostic Challenge Competition: Probabilistic Techniques For Fault Diagnosis In Electrical Power Systems, Brian W. Ricks, Ole J. Mengshoel

Ole J Mengshoel

Methods For Probabilistic Fault Diagnosis: An Electrical Power System Case Study, Brian Ricks, Ole J. Mengshoel

Ole J Mengshoel

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case …

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 …

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 …

A Generalized Neuron Based Adaptive Power System Stabilizer For Multimachine Environment, D. K. Chaturvedi, O. P. Malik

D. K. Chaturvedi Dr.

Artificial neural networks trained as intelligent controllers can easily accommodate the non-linearities and time dependencies of non-linear, dynamic systems. However, they require large training time and large number of neurons to deal with complex problems. Taking benefit of the characteristics of a generalized neuron (GN), that requires much smaller training data and shorter training time, a generalized neuron based adaptive power system stabilizer (GNAPSS) is proposed. It consists of a GN as an predictor, that predicts the plant dynamics, and a GN as a controller to damp low frequency oscillations. Results show that the proposed GNAPSS can provide a consistently …

Experimental Studies Of Generalized Neuron Based Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra

D. K. Chaturvedi Dr.

Artificial neural networks (ANNs) can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. However, they require large training time and large number of neurons to deal with complex problems. To overcome these drawbacks, a generalized neuron (GN) has been developed that requires much smaller training data and shorter training time. Taking benefit of these characteristics of the GN, a new power system stabilizer (PSS) is proposed. Results show that the proposed GN-based PSS can provide a consistently good dynamic performance of the system over a wide range …

New Neuron Model For Simulating Rotating Electrical Machines And Load Forecasting Problems, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra

D. K. Chaturvedi Dr.

The existing neuron structure has an aggregation function (usually summation) and its transformation through non-linear filter or squashing or thresholding functions. Such structure of neural networks has a number of disadvantages like large number of neurons, hidden layers and huge training data required for complex function approximations. The present paper proposes new neuron models to overcome the above problems in the existing neural networks. The model has been developed and tested for modelling of electrical machines like DC motor, induction motor and synchronous generator and load forecasting problems using different new neuron models in the neural network like S neuron …