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

Software And System Health Management For Autonomous Robotics Missions, Johann Schumann, Timmy Mbaya, Ole J. Mengshoel

Ole J Mengshoel

Advanced autonomous robotics space missions rely heavily on the flawless interaction of complex hardware, multiple sensors, and a mission-critical software system. This software system consists of an operating system, device drivers, controllers, and executives; recently highly complex AI-based autonomy software have also been introduced. Prior to launch, this software has to undergo rigorous verification and validation (V&V). Nevertheless, dormant software bugs, failing sensors, unexpected hardware-software interactions, and unanticipated environmental conditions—likely on a space exploration mission—can cause major software faults that can endanger the entire mission.

Our Integrated Software Health Management (ISWHM) system continuously monitors the hardware sensors and the software …

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

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

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, …

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 …

Towards Software Health Management With Bayesian Networks, Johann Schumann, Ole J. Mengshoel, Ashok Srivastava, Adnan Darwiche

Ole J Mengshoel

More and more systems (e.g., aircraft, machinery, cars) rely heavily on software, which performs safety-critical operations. Assuring software safety though traditional V&V has become a tremendous, if not impossible task, given the growing size and complexity of the software. We propose that iSWHM (Integrated SoftWare Health Management) can increase safety and reliability of high-assurance software systems. iSWHM uses advanced techniques from the area of system health management in order to continuously monitor the behavior of the software during operation, quickly detect anomalies and perform automatic and reliable root-cause analysis, while not replacing traditional V&V. Information provided by the iSWHM system …

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 …

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 …

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 …

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

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 …

Designing Resource-Bounded Reasoners Using Bayesian Networks: System Health Monitoring And Diagnosis, Ole J. Mengshoel

Ole J Mengshoel

In this work we are concerned with the conceptual design of large-scale diagnostic and health management systems that use Bayesian networks. While they are potentially powerful, improperly designed Bayesian networks can result in too high memory requirements or too long inference times, to they point where they may not be acceptable for real-time diagnosis and health management in resource-bounded systems such as NASA’s aerospace vehicles. We investigate the clique tree clustering approach to Bayesian network inference, where increasing the size and connectivity of a Bayesian network typically also increases clique tree size. This paper combines techniques for analytically characterizing clique …

Le Diagnostic Automatique Des Anomalies Des Systèmes Electromécaniques, Boukhemis Chetate

Boukhemis Chetate

Le fonctionnement optimal et continu des mécanismes industriels ne peut être envisagé sans la présence d’un système qui permet de prévenir (surveillance et interprétation) à l’état précoce les anomalies qui peuvent surgie au niveau des différents équipements et de diagnostiquer rapidement les pannes. Dans le système proposé en vue d’automatiser complètement le processus de diagnostic des ensembles moteurs asynchrones convertisseurs de fréquence et commande , et en vue de diminuer le temps d’analyse des défaillances et les coûts des interventions, le système expert est associé avec un sous –système de mesure, d’acquisition et de traitement de l’information (permettant l’observation des …