Qualitative event-based diagnosis applied to a spacecraft electrical power distribution system

Daigle, Matthew; Roychoudhury, Indranil; Bregón, Aníbal

doi:10.1016/j.conengprac.2015.01.007

Cited by 15 publications

(6 citation statements)

References 33 publications

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“…The ADAPT-Lite system is designed to emulate the operation of generic spacecraft electrical power distribution systems [30]. The system has five subsystems: (1) the battery, (2) the Direct Current (DC) electric load, (3) the inverter, (4) the Alternating Current (AC) resistive electric load, and (5) the electric fan as a second inductive load for the AC system (see Figure 11).…”

Section: Case Studymentioning

confidence: 99%

Structural Methodologies for Distributed Fault Detection and Isolation

2019

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The increasing complexity and size of cyber-physical systems (e.g., aircraft, manufacturing processes, and power generation plants) is making it hard to develop centralized diagnosers that are reliable and efficient. In addition, advances in networking technology, along with the availability of inexpensive sensors and processors, are causing a shift in focus from centralized to more distributed diagnosers. This paper develops two structural approaches for distributed fault detection and isolation. The first method uses redundant equation sets for residual generation, referred to as minimal structurally-over-determined sets, and the second is based on the original model equations. We compare the diagnosis performance of the two algorithms and clarify the pros and cons of each method. A case study is used to demonstrate the two methods, and the results are discussed together with directions for future work.

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Section: Case Studymentioning

confidence: 99%

Structural Methodologies for Distributed Fault Detection and Isolation

2019

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“…In previous works, health monitoring and diagnosis was applied to the K11 rover. In Narasimhan et al (2012), two diagnosis algorithms were applied, Qualitative Event-based Diagnosis (QED) [Daigle et al (2015a)], and the Hybrid Diagnosis Engine (HyDE) [Narasimhan and Browston (2007)]. QED performs diagnosis based on reasoning over symbols representing qualitative deviations of the sensor signals with respect to model-predicted values.…”

Section: Related Workmentioning

confidence: 99%

“…Other diagnosis works have been conducted on the rover [Balaban et al (2013)], such as the QED algorithm, described in Daigle et al (2015a) or the HyDe (Hybrid Diagnostic Engine) [Narasimhan and Brownston (2007)]. The main advantage of our approach is that the diagnosis estimates are not presented as a set of candidates, but as a distribution of candidates.…”

Section: Performance Analysis Comparison With Other Approachesmentioning

confidence: 99%

Diagnosis of Hybrid Systems Using Hybrid Particle Petri Nets: Theory and Application on a Planetary Rover

Gaudel

Chanthery

Ribot

et al. 2018

Fault Diagnosis of Hybrid Dynamic and Complex Systems

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This chapter presents a new methodology to perform health monitoring of hybrid systems under uncertainty. Hybrid systems can be represented as multi-mode systems with hybrid automata. Diagnosers are generated from these hybrid automata using a new data structure in order to monitor both the behavior and degradation of such systems. After a review of the state of the art on different existing solutions for diagnosis of hybrid systems under uncertainty, we propose to introduce the Hybrid Particle Petri Nets (HPPN) modeling framework. The main advantage of HPPN is that they take into account knowledge-based uncertainty in the system representation and uncertainty in the diagnosis process. The HPPN-based diagnoser deals with occurrences of unobservable discrete events (such as fault events) and it is robust to false observations. It also estimates the continuous state of the system by using particle filtering. A methodology is proposed to perform model-based diagnosis on hybrid systems by using the HPPN modeling framework. The system diagnosis is computed at any time from a HPPN-based diagnoser and contains all the hypotheses over its past mode trajectory. Each hypothesis is valued with a belief degree and includes discrete and continuous state estimates, as well as the set of faults that occurred on the system up to the current time. The HPPN-based methodology is demonstrated with an application on the K11 planetary rover prototype developed by NASA Ames Research Center. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.

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“…The current prototype implementation, meant to primarily test the PDM algorithms, includes only a minimal diagnostic functionality. Future versions will incorporate a fully-featured diagnoser, such as HyDE (Narasimhan & Brownston, 2007) or QED (Daigle & Roychoudhury, 2010). One of our long-term goals is to unify diagnostic and decision making processes around a single, comprehensive system model.…”

Section: Phm Reasoning Architecturementioning

confidence: 99%

A Modeling Framework for Prognostic Decision Making and its Application to UAV Mission Planning

Balaban

Alonso

2013

PHM_CONF

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The goal of prognostic decision making (PDM) is to utilize information on anticipated system health changes in selecting future actions. One of the key challenges in PDM is find- ing a sufficiently expressive yet compact mathematical representation of the system for use with decision optimization algorithms. In this paper we describe a general modeling approach for a class of PDM problems with non-linear system degradation processes and uncertainties in state estimation, action effects, and future operating conditions. The approach is based on continuous Partially Observable Markov Decision Processes (POMDPs) used in conjunction with ’black box’ system simulations. The proposed modeling framework can be cast into simpler representations, depending on which sources of uncertainty are being included. The approach is illustrated with a mission planning case study for an unmanned aerial vehicle (UAV). In the case study a PDM system is tasked with optimizing the vehicle route after an in-flight component fault is detected. A stochastic algorithm (based on particle filtering) is used for decision optimization, with a second, deterministic algorithm providing a performance evaluation baseline. Both algorithms utilize a UAV physics simulator for generating predictions of future vehicle states. Performance benchmarking is done on a set of mission scenarios of increasing complexity.

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Qualitative event-based diagnosis applied to a spacecraft electrical power distribution system

Cited by 15 publications

References 33 publications

Structural Methodologies for Distributed Fault Detection and Isolation

Structural Methodologies for Distributed Fault Detection and Isolation

Diagnosis of Hybrid Systems Using Hybrid Particle Petri Nets: Theory and Application on a Planetary Rover

A Modeling Framework for Prognostic Decision Making and its Application to UAV Mission Planning

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