Minimal Structurally Overdetermined sets for residual generation: A comparison of alternative approaches

Armengol, Joaquim; Bregón, Aníbal; Escobet, Teresa; Gelso, Esteban R.; Krysander, Mattias; Nyberg, Mattias; Olivé, Xavier; Pulido, Belarmino; Travé-Massuyès, Louise

doi:10.3182/20090630-4-es-2003.00241

Cited by 49 publications

(40 citation statements)

References 16 publications

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“…In practice r(z) will never be zero because of process noise, but this can be overcome by thresholding the value using statistical tests [18]. Two different approaches for creating residual generators are the parity space approach [31] and the structural approach [14]. For both these approaches different subsets of the equations in the system model are transformed to make the residual generators.…”

Section: Fault Detection and Isolationmentioning

confidence: 99%

“…It is also possible to have system models where faulty behavior is modeled explicitly [38,88]. The consistency-based diagnosis approach can also be combined with the FDI approach by creating residual generators such that each non-zero residual maps to a conflict set [14,33]. Consistency-based methods can then be used to compute consistent diagnoses.…”

Section: Consistency-based Diagnosismentioning

confidence: 99%

“…To disengage the retarder, the safety valve (10) is opened and the torus is drained of oil. The valves are controlled by the ECU through a pneumatic valve block (7) using inputs from sensors that measure the coolant temperature (13), the oil temperature (14), and the oil pressure (15). When modeling the retarder, the first step was to let experts identify all system components that were known to fail.…”

Section: The Retardermentioning

confidence: 99%

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Troubleshooting Trucks: Automated Planning and Diagnosis

Warnquist¹

2015

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This thesis considers computer-assisted troubleshooting of heavy vehicles such as trucks and buses. In this setting, the person that is troubleshooting a vehicle problem is assisted by a computer that is capable of listing possible faults that can explain the problem and gives recommendations of which actions to take in order to solve the problem such that the expected cost of restoring the vehicle is low. To achieve this, such a system must be capable of solving two problems: the diagnosis problem of finding which the possible faults are and the decision problem of deciding which action should be taken.The diagnosis problem has been approached using Bayesian network models. Frameworks have been developed for the case when the vehicle is in the workshop only and for remote diagnosis when the vehicle is monitored during longer periods of time.The decision problem has been solved by creating planners that select actions such that the expected cost of repairing the vehicle is minimized. New methods, algorithms, and models have been developed for improving the performance of the planner.The theory developed has been evaluated on models of an auxiliary braking system, a fuel injection system, and an engine temperature control and monitoring system.This work has been supported Scania CV AB and FFI -Fordonsstrategisk Forskning och Innovation. iii iv AcknowledgmentsDenna avhandling är slutet på en lång och spännande resa som inte hade varit möjligt utan hjälp. Arbetet med avhandlingen har varit en del av ett forskningssamarbete mellan fordonstillverkaren Scania och institutionen för datavetenskap vid Linköpings universitet.I Linköping vill jag tacka mina handledare Professor Patrick Doherty och Dr Jonas Kvarnström. Patrick har gett mig god träning i och verktyg för hur jag muntligt presenterar min forskning. Jonas har varit ett otroligt stöd vid framtagandet av nya forskningsidéer och forskningsartiklar. Med nästan övermänsklig noggrannhet kan hans skarpa ögon upptäcka minsta oklarhet i snåriga ekvationer. Jag är tacksam för all kunskap han delat med sig av. Jag har lärt mig mycket om hur man skriver vetenskapliga artiklar. Stort tack till Anne Moe, koordinator för doktorander på institutionen för datavetenskap. Hon är en klippa och har gett mig ovärderlig hjälp i den praktiska planeringen av framläggningarna av denna avhandling. Jag vill även tacka alla doktorander på KPLAB för alla spännande lunchdiskussioner.På Scania vill jag först och främst tacka Dr Mattias Nyberg som varit initiativtagare till forskningsprojekten och som gett mig förstklassig handledning. Mattias har tillsammans med Dr Jonas Biteus varit med och utvecklat tillämpningen av min forskning på Scania. Jag vill även tacka alla andra idésprutor som bidragit, i synnerhet Hans Ivendal, Dr Tony Lindgren, Dr Martin Eineborg och övriga i min v arbetsgrupp på Scania. Jag vill även tacka min styrgrupp och alla de skickliga chefer stöttat mig under denna tid: Göran Johansson, Thor Brenden, Per Karlsson, Anna Pernestål, Björn Bertmar och Erik Malmkvist.D...

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Section: Fault Detection and Isolationmentioning

confidence: 99%

Section: Consistency-based Diagnosismentioning

confidence: 99%

Section: The Retardermentioning

confidence: 99%

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Troubleshooting Trucks: Automated Planning and Diagnosis

Warnquist¹

2015

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“…Based on this graph a Dulmage-Mendelsohn decomposition (Dulmage and Mendelsohn, 1958) gives information about what part of the model that has analytical redundancy, and thereby can be monitored. There are several efficient tools available to find subsets of the model with analytical redundancy, and some of these are discussed and compared in Armengol et al (2009).…”

Section: Structural Analysismentioning

confidence: 99%

Model Based Vehicle Level Diagnosis for Hybrid Electric Vehicle

Sundström

2014

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When hybridizing a vehicle, new components are added that need to be monitored due to safety and legislative demands. Diagnostic aspects due to powertrain hybridization are investigated, such as that there are more mode switches in the hybrid powertrain compared to a conventional powertrain, and that there is a freedom in choosing operating points of the components in the powertrain via the overall energy management and still fulfill the driver torque request. A model of a long haulage truck is developed, and a contribution is a new electric machine model. The machine model is of low complexity, and treats the machine constants in a different way compared to a standard model. It is shown that this model describes the power losses significantly better when adopted to real data, and that this modeling improvement leads to better signal separation between the non-faulty and faulty cases compared to the standard model.To investigate the influence of the energy management design and sensor configuration on the diagnostic performance, two vehicle level diagnosis systems based on different sensor configurations are designed and implemented. It is found that there is a connection between the operating modes of the vehicle and the diagnostic performance, and that this interplay is of special relevance in the system based on few sensors.In consistency based diagnosis it is investigated if there exists a solution to a set of equations with analytical redundancy, i.e. there are more equations than unknown variables. The selection of sets of equations to be included in the diagnosis system and in what order to compute the unknown variables in the used equations affect the diagnostic performance. A systematic method that finds properties and constructs residual generator candidates based on a model has been developed. Methods are also devised for utilization of the residual generators, such as initialization of dynamic residual generators, and for consideration of the fault excitation in the residuals using the internal form of the residual generators. For demonstration, the model of the hybridized truck is used in a simulation study, and it is shown that the methods significantly increase the diagnostic performance.The models used in a diagnosis system need to be accurate for fault detection. Map based models describe the fault free behavior accurately, but fault isolability is often difficult to achieve using this kind of model. To achieve also good fault isolability performance without extensive modeling, a new diagnostic approach is presented. A map based model describes the nominal behavior, and another model, that is less accurate but in which the faults are explicitly included, is used to model how the faults affect the output signals. The approach is exemplified by designing a diagnosis system monitoring the power electronics and the electric machine in a hybrid vehicle, and simulations show that the approach works well. v Populärvetenskaplig SammanfattningEtt diagnossystem övervakar ett system för att faststä...

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“…Based on this graph a Dulmage-Mendelsohn decomposition (Dulmage and Mendelsohn, 1958) gives information about what part of the model that is overdetermined and thereby can be monitored. There are several efficient tools available to find subsets of the model with analytical redundancy, and some of these are discussed and compared in Armengol et al (2009). Overdetermined sets are of special interest since they are used to construct residuals, and are denoted e.g.…”

Section: Structural Analysismentioning

confidence: 99%

Residual Generator Selection for Fault Diagnosis of Hybrid Vehicle Powertrains

Sundström

Frisk

Nielsen

2011

IFAC Proceedings Volumes

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Abstract:The performance of a model based diagnosis system is affected by the selection of consistency relation in a set of equations with analytical redundancy in a non-linear system. To investigate aspects due to this, two diagnosis systems of a parallel hybrid truck are designed, and both static and dynamic issues are considered. A simplified vehicle model is used to exemplify how a unique expression for the residual generator can be found for one selection of consistency relation, but not for others, using the same set of equations. A simulation study using the entire vehicle model is made to investigate how the performance in the diagnosis system is affected when dynamic equations are either differentiated or integrated. The diagnosis systems are designed using structural analysis in combination with the algebraic expressions. One key result is that it is not trivial to find a computational order by hand that fulfills the predefined conditions on the computational sequence, and therefore systematic methods are valuable.

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Minimal Structurally Overdetermined sets for residual generation: A comparison of alternative approaches

Cited by 49 publications

References 16 publications

Troubleshooting Trucks: Automated Planning and Diagnosis

Troubleshooting Trucks: Automated Planning and Diagnosis

Model Based Vehicle Level Diagnosis for Hybrid Electric Vehicle

Residual Generator Selection for Fault Diagnosis of Hybrid Vehicle Powertrains

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