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A QUALITATIVE EVENT-BASED APPROACH TO FAULT DIAGNOSIS OF HYBRID SYSTEMS
, 2008
"... Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Many ..."
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Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Many
Fault Diagnosis of Continuous Systems Using Discrete-Event Methods
"... Abstract — Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Although discreteevent diagnosis methods are used extensively, they do not easily apply to parametric fault isolation in systems with complex continuous dynamics. This paper presents a novel discret ..."
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Cited by 7 (6 self)
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Abstract — Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Although discreteevent diagnosis methods are used extensively, they do not easily apply to parametric fault isolation in systems with complex continuous dynamics. This paper presents a novel discreteevent system diagnosis approach for abrupt parametric faults in continuous systems that is based on a qualitative abstraction of measurement deviations from the nominal behavior. Our approach systematically generates a diagnosis model from bond graphs that is used to analyze system diagnosability and derive the discrete-event diagnoser. The proposed approach is applied to an electrical power system diagnostic testbed. I.
Empirical Evaluation of Diagnostic Algorithm Performance Using a Generic Framework
- IN INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT
, 2010
"... A variety of rule-based, model-based and data-driven techniques have been proposed for detection and isolation of faults in physical systems. However, there have been few efforts to comparatively analyze the performance of these approaches on the same system under identical conditions. One reason fo ..."
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Cited by 6 (1 self)
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A variety of rule-based, model-based and data-driven techniques have been proposed for detection and isolation of faults in physical systems. However, there have been few efforts to comparatively analyze the performance of these approaches on the same system under identical conditions. One reason for this was the lack of a standard frame-work to perform this comparison. In this pa-per we introduce a framework, called DXF, that provides a common language to represent the system description, sensor data and the fault diagnosis results; a run-time architecture to execute the diagnosis algorithms under identical conditions and collect the diagnosis results; and an evaluation component that can compute performance metrics from the diagnosis results to compare the algorithms. We have used DXF to perform an empirical evaluation of 13 diagnostic algorithms on a hardware testbed (ADAPT) at NASA Ames Research Center and on a set of synthetic circuits typically used as bench-marks in the model-based diagnosis comm-nity. Based on these empirical data we analyze the performance of each algorithm and suggest directions for future development.
An Aircraft Electric Power Testbed for Validating Automatically Synthesized Reactive Control Protocols
, 2013
"... Modern aircraft increasingly rely on electric power for subsys-tems that have traditionally run on mechanical power. The complexity and safety-criticality of aircraft electric power sys-tems have therefore increased, rendering the design of these systems more challenging. This work is motivated by t ..."
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Cited by 4 (2 self)
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Modern aircraft increasingly rely on electric power for subsys-tems that have traditionally run on mechanical power. The complexity and safety-criticality of aircraft electric power sys-tems have therefore increased, rendering the design of these systems more challenging. This work is motivated by the potential that correct-by-construction reactive controller syn-thesis tools may have in increasing the effectiveness of the elec-tric power system design cycle. In particular, we have built an experimental hardware platform that captures some key elements of aircraft electric power systems within a simplified setting. We intend to use this platform for validating the ap-plicability of theoretical advances in correct-by-construction control synthesis and for studying implementation-related challenges. We demonstrate a simple design workflow from
Reactive Bayesian network computation using feedback control: An empirical study
- In Proc. of BMAW-12
, 2012
"... Abstract This paper investigates the challenge of integrating intelligent systems into varying computational platforms and application mixes while providing reactive (or soft real-time) response. We integrate Bayesian network computation with feedback control, thereby achieving our reactive objecti ..."
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Cited by 3 (3 self)
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Abstract This paper investigates the challenge of integrating intelligent systems into varying computational platforms and application mixes while providing reactive (or soft real-time) response. We integrate Bayesian network computation with feedback control, thereby achieving our reactive objective. As a case study we investigate fault diagnosis using Bayesian networks. While we consider the likelihood weighting and junction tree propagation Bayesian network inference algorithms in some detail, we hypothesize that the techniques developed can be broadly applied to achieve reactive intelligent systems. In this paper's empirical study we demonstrate reactive fault diagnosis for an electrical power system.
Integrating probabilistic reasoning and statistical quality control techniques for fault diagnosis in hybrid domains.
- In Proc. of the Annual Conference of the PHM Society 2011 (PHM11)
, 2011
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Adaptive control of Bayesian network computation
- In Proc. of 2012 - 5th International Symposium on Resilient Control Systems
, 2012
"... Abstract-This paper considers the problem of providing, for computational processes, soft real-time (or reactive) response without the use of a hard real-time operating system. In particular, we focus on the problem of reactively computing fault diagnosis by means of different Bayesian network infe ..."
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Abstract-This paper considers the problem of providing, for computational processes, soft real-time (or reactive) response without the use of a hard real-time operating system. In particular, we focus on the problem of reactively computing fault diagnosis by means of different Bayesian network inference algorithms on non-real-time operating systems where low-criticality (background) process activity and system load is unpredictable. To address this problem, we take in this paper a reconfigurable adaptive control approach. Computation time is modeled using an ARX model where the input consists of the maximum number of background processes allowed to run at any given time. To ensure that the reactive (high-criticality) diagnosis is computed within a set time frame, we introduce a minimum degree pole placement controller to impose a limit on the maximum number of low-criticality processes. Experimentally, we perform electrical power system diagnosis using a Bayesian network model of and data from a NASA electrical power network. The Bayesian network inference algorithms likelihood weighting and junction tree propagation are successfully applied and changed mid-simulation to investigate how inference computation time changes in an unpredictable operating system, as well as how the controller reacts to inference algorithm changes.
Simulation-Based Design of Aircraft Electrical Power Systems
"... Early stage design provides the greatest opportunities to explore design alternatives and perform trade studies before costly design decisions are made. The goal of this research is to develop a simulation-based framework that enables architectural analysis of complex systems during the conceptual d ..."
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Cited by 2 (0 self)
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Early stage design provides the greatest opportunities to explore design alternatives and perform trade studies before costly design decisions are made. The goal of this research is to develop a simulation-based framework that enables architectural analysis of complex systems during the conceptual design phase. Using this framework, design teams can systematically explore architectural design decisions during the early stage of system development prior to the selection of specific components. The analysis performed at this earliest stage of design facilitates the development of more robust and reliable system architectures. Application of the presented method to the design of a representative aerospace electrical power system (EPS) demonstrates these capabilities.
Towards real-time, on-board, hardware-supported sensor and software health management for unmanned aerial systems
- in Proceedings of the 2013 Annual Conference of the Prognostics and Health Management Society (PHM2013
, 2013
"... Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while main-taining safety with respect to other aircraft as well as hu-mans and property on the ground. In this paper, we design a real-t ..."
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Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while main-taining safety with respect to other aircraft as well as hu-mans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) ca-pability to continuously monitor sensors, software, and hard-ware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, provid-ing: (1) real-time monitoring of sensor and/or software sig-nals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power real-
Model-based diagnostic decision-support system for satellites
- In IEEE Aerospace Conference
, 2013
"... Abstract—We propose a novel framework for Model-Based Di-agnosis (MBD) that uses active testing to decrease the diagnostic uncertainty. This framework is called LYDIA-NG and combines several diagnostic, simulation, and active-testing algorithms. We have illustrated the workings of LYDIA-NG by buildi ..."
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Abstract—We propose a novel framework for Model-Based Di-agnosis (MBD) that uses active testing to decrease the diagnostic uncertainty. This framework is called LYDIA-NG and combines several diagnostic, simulation, and active-testing algorithms. We have illustrated the workings of LYDIA-NG by building a LYDIA-NG-based decision support system for the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite. This paper discusses a model of the GOCE Electrical Power System (EPS), the algorithms for diagnosis and disambiguation, and the experiments performed with a number of diagnostic sce-narios. Our experiments produced no false positive scenarios, no false negative scenarios, the average number of classification errors per scenario is 1.25, and the fault detection time is equal to the computation time. We have further computed an average fault uncertainty of 2.06 × 10−3 which can be automatically re-
