Jose R. Celaya

Modeling and analysis of multi-agent systems using petri nets

The development of theoretical-based methods for the assessment of multi-agent systems properties is of critical importance. This work is a preliminary investigation on methodologies for modeling, analysis and design of multi-agent systems. Multi-agent systems are regarded as discrete-event dynamic systems and Petri nets are used as a modeling tool to assess the structural properties of the multi-agent system. Our methodology consists of defining a simple multi-agent system based on the abstract architecture for intelligent agents. The abstract architecture is modeled as a discrete-event system using Petri nets and structural analysis of the net provides an assessment of the communication and coordination properties of the multi-agent system. Deadlock avoidance in the multi-agent system is considered as an initial key property, and it is evaluated using liveness and boundedness properties of the Petri net model.

Requirements Flowdown for Prognostics and Health Management

Prognostics and Health Management (PHM) principles have considerable promise to change the game of lifecycle cost of engineering systems at high safety levels by providing a reliable estimate of future system states. This estimate is a key for planning and decision making in an operational setting. While technology solutions have made considerable advances, the tie-in into the systems engineering process is lagging behind, which delays fielding of PHM-enabled systems. The derivation of specifications from high level requirements for algorithm performance to ensure quality predictions is not well developed. From an engineering perspective some key parameters driving the requirements for prognostics performance include: (1) maximum allowable Probability of Failure (PoF) of the prognostic system to bound the risk of losing an asset, (2) tolerable limits on proactive maintenance to minimize missed opportunity of asset usage, (3) lead time to specify the amount of advanced warning needed for actionable decisions, and (4) required confidence to specify when prognosis is sufficiently good to be used. This paper takes a systems engineering view towards the requirements specification process and presents a method for the flowdown process. A case study based on an electric Unmanned Aerial Vehicle (e-UAV) scenario demonstrates how top level requirements for performance, cost, and safety flow down to the health management level and specify quantitative requirements for prognostic algorithm performance.

Defect classification of highly noisy NDE data using classifier ensembles

In this paper, we present a feature selection and classification approach that was used to assess highly noisy sensor data from a NDE field study. Multiple, heterogeneous NDT sensors were employed to examine the solid structure. The goal was to differentiate between two types of phenomena occurring in a solid structure where one phenomenon was benign, the other was malignant. Manual distinction between these two types is almost impossible. To address these issues, we used sensor validation techniques to select the best available sensor that had the least noise effects and the best defect signature in the region of interest. Hundreds of features were formulated and extracted from data of the selected sensors. Next, we employed separability measures and correlation measures to select the most promising set of features. Because the NDE sensors poorly described the different defect types under consideration, the resulting features also exhibited poor separability. The focus of this paper is on how one can improve the classification under these constraints while minimizing the risk of overfitting (the number of field data was small). Results are shown from a number of different classifiers and classifier ensembles that were tuned to a set true positive rate using the Neyman-Pearson criterion.

A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

A Briefing on Metrics and Risks for Autonomous Decision-Making in Aerospace Applications

Significant technology advances will enable future aerospace systems to safely and reliably make decisions autonomously, or without human interaction. The decision-making may result in actions that enable an aircraft or spacecraft in an off-nominal state or with slightly degraded components to achieve mission performance and safety goals while reducing or avoiding damage to the aircraft or spacecraft. Some key technology enablers for autonomous decision-making include: a continuous state awareness through the maturation of the prognostics health management field, novel sensor development, and the considerable gains made in computation power and data processing bandwidth versus system size. Sophisticated algorithms and physics based models coupled with these technological advances allow reliable assessment of a system, subsystem, or components. Decisions that balance mission objectives and constraints with remaining useful life predictions can be made autonomously to maintain safety requirements, optimal performance, and ensure mission objectives. This autonomous approach to decision-making will come with new risks and benefits, some of which will be examined in this paper. To start, an account of previous work to categorize or quantify autonomy in aerospace systems will be presented. In addition, a survey of perceived risks in autonomous decision-making in the context of piloted aircraft and remotely piloted or completely autonomous unmanned autonomous systems (UAS) will be presented based on interviews that were conducted with individuals from industry, academia, and government.

Multi-criteria Set Partitioning for Portfolio Management: A Visual Interactive Method

A visual interactive multi-criteria decision-making method for partitioning a portfolio of assets into mutually exclusive categories is presented. The two principal decision categories are hold and sell - portfolio assets in the sell category are considered as potential sale prospects, and the other assets in the portfolio are considered as potential retention prospects. The problem may be mathematically formulated as a multi-criteria 0/1 knapsack problem with multiple constraints. The decision-making method centers on the utilization of several coupled 2D projections of the portfolio in the multi-dimensional criterion space. The decision-maker interacts with these projections in a variety of ways to express and record multi-category (hold, hold-bias, sell-bias, and sell) set partitioning preferences. The decision-maker may also set an aggregated preference threshold that is utilized for partitioning the portfolio into the two principal hold and sell categories. The decision-maker may further fine-tune their preferences and threshold settings so as to achieve a multitude of financial targets.

Physics based Degradation Modeling and Prognostics of Electrolytic Capacitors under Electrical Overstress Conditions

This paper proposes a physics based degradation modeling and prognostics approach for electrolytic capacitors. Electrolytic capacitors are critical components in electronics systems in aeronautics and other domains. Degradations in capacitor and MOSFET components are often the cause of failures in DC-DC converters. For example, prevalent fault effects, such as a ripple voltage surge at the power supply output, can damage interconnected critical subsystems leading to cascading fault propagation. Prognostics in general and in this case electronics components in particular is concerned with the prediction of remaining useful life (RUL) of components and systems. It performs a condition-based health assessment by estimating the current state of health. Furthermore, it leverages the knowledge of the device physics and degradation physics to predict remaining useful life as a function of current state of health and anticipated operational and environmental conditions. Physics-based models capture degradation phenomena in terms of component geometry and energy based principles that define the effect of stressors on the component behavior. This is in contrast to the traditional approach for deriving degradation models from empirical data. Implementing the degradation modeling techniques present a general methodology for estimating lifetimes due to specific failure mechanisms. The failure rate models can be tuned to include parameters that relate to the present health of the device/system and the expected conditions under which it will be operated. The models and algorithms are applied to data from degradation experiments of several COTS capacitors. Results show the efficiency of the approach chosen.

Modeling and Analysis Methods for Multi-agent Systems

architecture for intelligent agents, and then constructing a Petri net model systematically from the abstract architecture description of the system. Different scenarios were considered in order to indicate how the Petri net analysis methodologies can be used to assess key system properties, showing that there is a relationship between multi-agent systems with indirect interaction and Petri nets. The mapping of multi-agent systems models with the abstract architecture into Petri net models captured the discrete-event dynamics of the systems under consideration. This allowed the assessment of properties like deadlock avoidance in a multi-agent system, which can be assessed systematically from the Petri net model. In general, the results presented in this work show the potential for using Petri nets to assess key properties of multi-agent systems. In addition, they provide the foundation to further investigate the application of Petri net methodologies for the modeling, analysis and design of multi-agent systems.

Sensor validation in non-destructive evaluation using clustering

Non-destructive evaluation (NDE) techniques for condition monitoring in remote solid structures have evolved vastly in the last few years. Algorithms for estimation of sensor integrity and for noise correction form a crucial aspect of NDE. This paper presents a sensor validation approach that verifies sensor integrity, identifies and corrects noise effects and selects the best possible array of sensors for multi-sensor fusion. The proposed methodology uses a novel change detection algorithm for noise correction and a clustering algorithm to isolate useful signal information from the sensor data. It was used for sensor selection in a NDE field study, where multiple sensors were used to examine a solid structure. The methodology achieved 97% accuracy in the experiments, indicating its efficacy.