Qualitative Modeling is the Key to Automated Diagnosis

Struß, Peter; Malik, Andreas; Sachenbacher, Martin

doi:10.1016/s1474-6670(17)58702-9

Cited by 19 publications

(15 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following a modeling approach similar to the one described in [23], we model the connectivity to source (i.e. pump) and sink for each component terminal.…”

Section: An Example Systemmentioning

confidence: 99%

Parametric abstraction of behavioral modes for model-based diagnosis

Torta¹,

Torasso

2009

AI Communications

View full text Add to dashboard Cite

In the present paper we address the problem of automatically abstracting the behavioral modes of system components on the basis of their indiscriminability in a diagnostic setting. Our goal is to abstract the original model in such a way as to provide more informative results to the supervisor, without loosing any relevant diagnostic information.This paper extends and complements existing work on automatic abstraction in MBD in different directions: we propose a framework to integrate different parameters (system observability, context restriction and status restriction) that can influence the abstractions; we develop an algorithm for the computation of abstractions that can take advantage of the symbolic compilation of the system model for giving both theoretical guarantees about the computational cost and good experimental performance on non-trivial domains; finally, we discuss the properties of the abstractions resulting by the combination of a-priori, user-provided abstractions with the ones automatically computed by our algorithm.

show abstract

“…Following a modeling approach similar to the one described in [23], we model the connectivity to source (i.e. pump) and sink for each component terminal.…”

Section: An Example Systemmentioning

confidence: 99%

Parametric abstraction of behavioral modes for model-based diagnosis

Torta¹,

Torasso

2009

AI Communications

View full text Add to dashboard Cite

show abstract

“…Exploiting these structure trees, qualitative behavior can in fact be computed by local propagation. Unlike other approaches such as QCAT [8], the SPS method [6], and the Connectivity method [10], MAD offers certain features to improve the accuracy of qualitative prediction. This is explained in the following.…”

Section: Computation Of Qualitative Valuesmentioning

confidence: 99%

MAD: A Real World Application of Qualitative Model-Based Decision Tree Generation for Diagnosis

Milde

Hotz

Kahl

et al. 1999

Multiple Approaches to Intelligent Systems

View full text Add to dashboard Cite

Abstract.Computer diagnosis systems grounded on hand-crafted decision trees are wide-spread in industrial practice. Since the complexity of technical system increases and innovation cycles are shortened, the need for systematic decision tree generation and maintenance arises. In this paper, the MAD system is introduced which generates decision trees based on qualitative device models. Existing resources such as design data and expert design know-how as well as decision trees and diagnosis knowledge can easily be reused and integrated into decision tree generation. Since decision tree generation is based on device models, applying MAD reduces average fault identification cost and facilitates quality management of diagnosis equipment. Furthermore, cost of diagnosis system generation, modification and maintenance is reduced. We have successfully evaluated the MAD system in cooperation with the german forklift manufacturer STILL GmbH Hamburg.

show abstract

“…However, the design of the diagnostic strategy is often made di cult by the complexity of the system, which can limit the use of quantitative model-based approaches requiring a very detailed and well-tuned analytical model of the whole plant, such as the methods described in Gertler (1998), Patton et al (1989), Isermann (1993), Park et al (1994) and Kinnaert (1999). Techniques relying on more qualitative models, such as those based on arti®cial intelligence (Kleer et al 1992, Struss et al 1996, fault trees (De Vries 1990, Lee et al 1985, Pages and Gondran 1986 or propagation digraphs (Iri et al 1979, Kokawa et al 1983, Koscielny 1995, Scattolini 2000, usually do not possess the characteristics of modularity and¯exibility that play a crucial role in the development and maintenance of an e ective design methodology widely applicable in an industrial environment.…”

Section: Introductionmentioning

confidence: 99%

Design methodology for diagnostic strategies for industrial systems

Magni¹,

Scattolini²,

Rossi³

2002

International Journal of Systems Science

View full text Add to dashboard Cite

This paper presents a method for the construction of diagnostic systems for complex industrial applications. The approach has been explicitely developed to shorten the design cycle and meet some speci®c requirements, such as modularity,¯exibility, and the possibility of merging many di erent sources of information. The method allows one to consider multiple simultaneous failures and is speci®cally designed to make easier the coordination and simpli®cation of local diagnostic algorithms developed by di erent teams.

show abstract

Qualitative Modeling is the Key to Automated Diagnosis

Cited by 19 publications

References 1 publication

Parametric abstraction of behavioral modes for model-based diagnosis

Parametric abstraction of behavioral modes for model-based diagnosis

MAD: A Real World Application of Qualitative Model-Based Decision Tree Generation for Diagnosis

Design methodology for diagnostic strategies for industrial systems

Contact Info

Product

Resources

About