Diagnostics and a qualitative model
Introduction
A diagnostic problem is a determination of those components of a system, which, if assumed to be functioning abnormally, will explain the discrepancy between the observed and the correct system behavior [1].
Diagnostic reasoning in the first generation expert systems was using shallow knowledge based on heuristic information to solve a diagnostic problem. However, the first generation expert systems have significant difficulties expressing the knowledge that is necessary to understand the interactions between the irregularities. The fundamental problem is that they do not understand the processes that underlie the observable findings. Therefore, they do not understand how the mechanisms in their irregular states function to produce the observed behavior [2].
To avoid these disadvantages, we need an approach that can explain how the normal mechanism responds to different changes and how a faulty mechanism might produce the observed manifestations. These demands can be met by using deep knowledge. The only available information in this case is the system description (its design or structure), together with the observation of the system behavior. This type of diagnostic reasoning is called model-based diagnostics.
The result of diagnostic reasoning is a set of declarations about components’ behavior called diagnosis, which could explain unexpected system behavior. Generally, there may be several competing diagnoses, which explain the same faulty system. However, only a unique diagnostic result ensures a proper intervention on the system to restore its normal behavior, what is the final goal of diagnostics. To discriminate among all competing diagnoses, an additional observation also called measurement (medical test or examination) is needed. For efficiency reasons, it is necessary to consider the previous diagnoses for computing new diagnoses for a given new measurement.
A theory of diagnoses presented in Reiter's framework [1] is adopted as the fundamental theoretical contribution to the model-based diagnosis. Hou later proposed a general theory of measurement in model-based diagnoses and provided a formal justification for his theory [3]. We propose a diagnostic architecture DISY for computing new diagnoses with a given new measurement, based on the previous diagnoses, adjusted for simulation process using the system model. For a formal argumentation of the presented diagnostic architecture and the role of the system model, we developed an original theoretical framework of a diagnostics.
Section snippets
Formulation of a diagnostics
As many researchers, we have developed a theory for diagnostics using a representation language based on first-order logic [4]. As a starting point to formalize our diagnostic approach, we have used Reiter's definition of a system, components, and an observation [1]:
- •
SD, the system description, is a set of first-order sentences;
- •
COMP, the system components, is a finite set of constants;
- •
OBS, the observations of a system, is a set of first-order sentences.
The system description SD describes a
Designing of a system model
Selection of a proper strategy for computing all diagnoses is an important task in a design of a diagnostic system. In this chapter, we define a qualitative system model for diagnostic reasoning based on testing the satisfiability according to the Definition 3.
Diagnostic architecture DISY
This chapter describes the diagnostic system DISY (Fig. 3), the architecture of which is based on the given theoretical framework. The central module of the architecture is the qualitative system model QSM presented in Chapter 3. Modules UD, DIS and DSM are databases for storing different temporary sets:
- •
UD contains all different sets of components, which will be tested for a potential diagnosis.
- •
DIS stores all competing sets that compose the diagnostic space.
- •
DSM contains sets that compose the
Conclusions
First, we want to point out that the main purpose of our contribution was to present the original formal concept of a model-based diagnostic approach. Novel contributions to the theory of diagnostics are summarized as follows:
- •
Definition of a diagnosis that directly expresses the function mode for all system components.
- •
Concept of measurement that takes into accounts the previous diagnostic result to obtain a new one from additional observation-measurement of the system.
- •
Introduction of a
References (11)
A theory of diagnosis from first principles
Artif. Intell.
(1987)A theory of measurement in diagnosis from first principles
Artif. Intell.
(1994)- et al.
Characterizing diagnoses and systems
Artif. Intell.
(1992) - et al.
A qualitative physics based on confluences
Artif. Intell.
(1984) Qualitative process theory
Artif. Intell.
(1984)
Cited by (5)
Integration of a laser interferometer and a CMM into a measurement system for measuring internal dimensions
2011, Measurement: Journal of the International Measurement ConfederationCitation Excerpt :After a few introductory measurements it was established that the probing head is perfectly stabilized in its zero position during a period of 20 s after the surface is probed. Thirty measurements were then performed under defined conditions [8–10]. The results are shown in Fig. 3.
Fault diagnosis design based on qualitative simulation
2012, ICICIP 2012 - 2012 3rd International Conference on Intelligent Control and Information ProcessingFault qualitative diagnosis model for thrusters of underwater vehicle
2011, Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and TechnologyAgent oriented approach to handling medical data
2005, Journal of Medical SystemsVerification of the conventional measuring uncertainty evaluation model with Monte Carlo simulation
2005, International Journal of Simulation Modelling