Efficiency Issues in Model-Based Approaches to On-Board Diagnosis

October 17-19, 2001 ESA Workshop “On-Board Autonomy”

Efficiency Issues in Model-Based Approaches

to On-Board DiagnosisP.Torasso, C.Picardi and L. Console

Dipartimento di Informatica- Universita’ di TorinoItaly


Autonomy and Diagnosis

• Onboard autonomy requires several components: planning and scheduling are very crucial

• Autonomy requires also the ability of detecting what is going wrong and reacting

• Diagnostic reasoning is an important step• Usually several competing diagnostic hypotheses• Need of informing the human operator about

diagnostic conclusions?• Need of informing other software components


Knowledge-based approaches to diagnosis

• Develop a domain theory for the system to be diagnosed

• Logical/qualitative models• Correct and/or faulty behavior • Usually component oriented• Reasoning mechanisms for prediction and post-

diction• In-depth understanding of diagnostic reasoning

(theory of diagnosis)


Challenges of On-board Diagnosis (I)

Off-board diagnosis is an iterative process• Available observations are used for making

hypotheses about faults• Model-based approaches to diagnosis are able to

suggest probes• Revision of the diagnostic hypotheses on the basis

of new measurements• Strong interaction with the human agent

On-board diagnosis has different requirements


Challenges of On-board Diagnosis (II)

• Observations provided just via sensors• Strict time constraints for producing diagnostic

conclusions• Difficulty/impossibility of acquiring extra data• Usually a large number of potential diagnoses• Need of complex reasoning mechanisms, very

expensive from a computational point of view• Sometimes, on-board computation resources are

limited


Efficiency issues

• Decomposing static and time-varying aspects in diagnostic problem solving

[Console et al. , Ann.Math&AI, 94] [Williams&Nayak, AAAI96] [Struss et al., DX96]

• Exploiting observations for decomposing diagnostic problems [e.g. Darwiche IJCAI 99]

• Exploiting hierarchical models for focussing diagnostic reasoning

[Mozetic, IJMMS 91], [Out et al., Ann.Math&AI 94, Renon,

IJCAI01]


Towards On-board Diagnosis

At Università di Torino work on on-board diagnosis in the automotive sector:

• European projects VMBD and IDD for the automotive sector

in the space sector • ASI sponsored basic research project on “An

Intelligent System for Supervising Autonomous Space Robots.” in co-operation with other Italian universities, CNR-IP


The compilation approach to on-board diagnosis (I)

• Developed inside VMBD project [Cascio et al. AI Comm 99, Console et al, IJCAI

01]

• Use of quite expressive qualitative models taking into account temporal and dynamic aspects of the system to be diagnosed

• Diagnostic reasoning on such models too expensive for on-board computing

• Off-line qualitative simulation of interesting diagnostic cases


The compilation approach to on-board diagnosis (II)

• Automatic learning of decision trees starting from diagnostic cases obtained via simulation and fault injection

• Use of the decision tree for on-board diagnosis ==> fast and small, suitable for current ECU

• The nodes of the decision tree contain measurements provided by sensors in different time points and leaves contain (repair) actions

• Discrimination among faults done as long as different actions can be done

• Discriminative power limited by lack of sensors


Exploring diagnostic problem solving: the SPIDER case study

• Investigated within a ASI supported project [Portinale&Torasso ISAIRAS 99, IJCAI 99]

• A logical model relating behavioral modes of components of the SPIDER arm and contextual information with qualitative manifestations (based on FMECA documents available)

• Innovative diagnostic strategies based on the notion of “Variable assignment problems”

• Exploiting analogies with constraint satisfaction problem for a compact representations of a set of diagnoses (“scenario”)


Diagnostic AgentDiagnosis as explanation of observations• DT behavioral model of SPIDER (nominal and faulty modes)• COMP the set of the components in SPIDER• CXT ground atoms modeling contextual information• OBS ground atoms representing observations


Diagnostic Agent (II)For most diagnostic problems a large number of diagnoses

Preference criterion: based on a measure related to MDL

Preferred solutions involving OK mode for most components

In many cases no sufficient information for discriminating among alternative (preferred) diagnoses


A diagnostic case where only qualitative measurements provided sensors are available


Compact representation of multiple-fault diagnoses for the diagnostic case (only sensor data)


The same diagnostic case where all qualitative measurements are available


One multiple-fault diagnosis for the previous diagnostic case (all observations are available)


Discriminability among diagnosesGoal: Formalize the notion that two diagnoses cannot be discriminated using a given type of measurements

Classes of observations: sensorized vs not sensorized

In SPIDER domain several classes: joint positions, temperature sensors, observations under human control


Discriminability among diagnoses (II)

Single out the behavioral modes of the components which cannot be discriminated using a class of manifestations

Independent on the specific diagnostic problem at hand


Finding indistinguishable behavioral modes• Injecting fault bmr (and bms ) for component Ci

• Evaluate the transitive closure for all possible contexts• Check if any difference is observable with meaurements in class

CLj


Automatic generation of abstract models

If diagnostic problem DP involves just manifestations of class i, some faults may be indistinguishable ==>

• Generation of alternative diagnoses that cannot be discriminated

• Computation time is consumed• Human or artificial supervisor may be confusedProposed solution:

automatic generation of abstract models where indistinguishable modes are collapsed


Automatic generation of abstract models (II)

A simplied form of the algorithm for:• collapsing behavioral modes • revising domain theory


Automatic generation of abstract models (III)

Off-line:generate an abstract model for each class of measurements (if there are indistinguishable modes)

On-line:for each diagnostic problem DP, select (from the model library) the model which fits available measurements

Benefits:• saving in computation time• reduction in the number of diagnoses

(if bmr and bms for component Ci are indistinguishable instead of two diagnoses just one involving bmr+s )

• no loss of information


Abstract models and abstraction of manifestations


using the abstract model just single fault diagnoses


Without abstract model, generation of multiple faults diagnoses with very low plausibility


Conclusions (I)

On-board diagnosis is an interesting and challenging problem

Need of multiple approaches:• efficient diagnostic strategies • partitioning the diagnostic problem• compilation techniques• automatic generation of abstract models if modes

are indistinguishable • taking recovery into consideration


Conclusions (II)

Interesting results obtained in the automotive sector• compilation techniques allow to put on-board

diagnostic procedures which are automatically generated starting from a model of the artifact

• diagnosability is an important aspect


Conclusions (III)• Efficiency is just one aspect• How to involve the human supervisor in the loop?

==> Interactive autonomy• How to make the result of the diagnostic agent

understandable to the human supervisor?

Some work in:• compact graphical representation of diagnoses• preference criteria among diagnoses• informing the supervisor about the impossibility

of discriminating among diagnoses

Efficiency Issues in Model-Based Approaches to On-Board Diagnosis

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