A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies
Introduction
Diagnostic activity comprises of two important components: a priori domain knowledge and search strategy. The basic a priori knowledge that is needed for fault diagnosis is a set of failures and the relationship between the observations (symptoms) and the failures. A diagnostic system may have them explicitly (as in a table look-up), or it may be inferred from some source of domain knowledge. A priori domain knowledge may be developed from a fundamental understanding of the process using first-principles knowledge. Such knowledge is referred to as deep, causal or model-based knowledge (Milne, 1987). On the other hand, it may be gleaned from past experience with the process. This knowledge is referred to as shallow, compiled, evidential or process history-based knowledge.
The model-based a priori knowledge can be broadly classified as qualitative or quantitative. The model is usually developed based on some fundamental understanding of the physics of the process. In quantitative models this understanding is expressed in terms of mathematical functional relationships between the inputs and outputs of the system. In contrast, in qualitative models these relationships are expressed in terms of qualitative functions centered around different units in a process. The qualitative models can be developed either as qualitative causal models or abstraction hierarchies. Fig. 1 shows the taxonomy of domain knowledge based on these two broad categories. In this part of the paper we will review the various qualitative knowledge forms shown in Fig. 1.
There are fundamentally two different approaches to search in fault diagnosis (Rasmussen, 1986): topographic search and symptomatic search. Topographic searches perform malfunction analysis using a template of normal operation, whereas, symptomatic searches look for symptoms to direct the search to the fault location. Fig. 2 shows a classification of diagnostic systems based on the search methods they employ. In this paper we discuss the various search methods as shown in Fig. 2.
Section snippets
Qualitative models
The development of knowledge-based expert systems was the first attempt to capture knowledge to draw conclusions in a formal methodology. An expert system is a computer program that mimics the cognitive behavior of a human expert solving problems in a particular domain. It consists of a knowledge base, essentially a large set of if–then–else rules and an inference engine which searches through the knowledge base to derive conclusions from given facts. Also, the tree of these if–then–else
Typology of diagnostic search strategies
There are fundamentally two different approaches to search in fault diagnosis (Rasmussen, 1986): topographic search, and symptomatic search. Topographic searches perform malfunction analysis using a template of normal operation, whereas, symptomatic searches look for symptoms to direct the search to the fault location. Fig. 2 shows a classification of diagnostic systems based on the search methods they employ.
Conclusions
In this second part, of three parts, of review paper, various forms of qualitative models such as causal models and abstraction hierarchies were reviewed. Though qualitative models have a number of advantages as discussed in this paper, the major disadvantage is the generation of spurious solutions. Considerable amount of work has been done in the reduction of the number of spurious solutions while reasoning with qualitative models. In SDGs, this is done using generation of latent constraints
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