Case-Based Reasoning for Automatic Interpretation of Data from Eddy-Current Inspection

In the design of the eddy-current inspection systems that have been reported to be able to interpret EC data automatically one can distinguish use of two methodologies. One is the use of classifiers to assign the signals to several predefined defect classes. Another is the use of expert systems to reason about the shape and other parameters of the signals in order to determine the defect types they represent. Both sorts of systems are usually designed with a specific inspection type in mind (e.g. steam generators of nuclear power plants). Adapting these systems to a different inspection type requires a considerable effort; therefore, they are generally not suitable for application in (petro-) chemical industry where heat-exchanger types vary from one inspection to another. This paper suggests case-based reasoning (CBR) as a methodology which is well suited for s ach applications. In this respect, one of the most important advantages of CBR systems is their ability to leam during use

Genetic algorithms for feature selection and weighting

Abstract Automated techniques to optimise the retrieval of relevant cases in a CBR system are desirable as a way to reduce the expensive knowledge acquisition phase. This paper concentrates on feature selection methods that assist in indexing the case-base, and feature weighting methods that improve the similarity-based selection of relevant cases. Two main types of method are presented: filter methods use no feedback from the learning algorithm that will be applied; wrapper methods incorporate feedback and hence take account of learning bias. Wrapper methods based on Genetic Algorithms have been found to deliver the best results with a tablet design application, but these generic methods are flexible about the criterion to be optimised, and should be applicable to a wide variety of problems. Introduction The majority of CBR systems rely on a good case-base organisation, an effective index and a (possibly knowledge intensive) similarity matching to select cases, that can then be used to solve a problem, see Many CBR tools provide standard means of constructing indexes. Isoft's ReCall is typical in using a C4.5 [Quinlan 1993] generated decision tree, constructed from the cases in the case-base, as the index. However, induction algorithms like C4.5 apply a greedy selection approach and so the features used by the index are not always the optimal ones. This is a particular problem when the cases contain many features irrelevant to the problem solving The cases identified by the index are next ranked according to their similarity to the new problem. The simplest similarity metric is Euclidean distance between normalised feature vectors. However, a "useful" (from the point of view of solving a problem) similarity should take account of the relative importances of various features. Certainly in a situation where many features are irrelevant to the problem to be solved, a simple similarity measure is insufficient. This problem can be partially solved by identifying and removing irrelevant features as before. However, a more flexible method assigns weights to the features to indicate their relative importance to the problem solving. Although the selection of the relevant features can usually be done quite accurately by an expert, feature weighting can only be done approximately by an expert, often by categorising the relevance as one from a small set of possible degrees of relevance. Therefore, applying an automated algorithm to find feature weights is attractive. Section 2 reviews feature selection and weighting methods. Our tablet formulation problem domain is introduced in Section 3

Feature Selection and Generalisation for Retrieval of Textual Cases

Textual CBR systems solve problems by reusing experiences that are in textual form. Knowledge-rich comparison of textual cases remains an important challenge for these systems. However mapping text data into a structured case representation requires a significant knowledge engineering effort. In this paper we look at automated acquisition of the case indexing vocabulary as a two step process involving feature selection followed by feature generalisation. Boosted decision stumps are employed as a means to select features that are predictive and relatively orthogonal. Association rule induction is employed to capture feature co-occurrence patterns. Generalised features are constructed by applying these rules. Essentially, rules preserve implicit semantic relationships between features and applying them has the desired effect of bringing together cases that would have otherwise been overlooked during case retrieval. Experiments with four textual data sets show significant improvement in retrieval accuracy whenever gener¬alised features are used. The results further suggest that boosted decision stumps with generalised features to be a promising combination

Acquisition of Adaptation Knowledge for Breast Cancer Treatment Decision Support

Colloque avec actes et comité de lecture. internationale.International audienceThe elaboration of a treatment in cancerology depends on the particular practice of decision protocols. These protocols are often adapted rather than used straightforwardly. This paper deals with the acquisition of the knowledge exploited during protocol adaptations. It shows that this knowledge acquisition process can be based on similarity paths, that are used for representing the matchings between decision problems (e.g., source and target problems within a case-based reasoning process)

Case-Based Reasoning for NDT Data Interpretation

Electrical Engineering, Mathematics and Computer Scienc

Case-Based Reasoning for Automatic Interpretation of Data from Eddy-Current Inspection

In the design of the eddy-current inspection systems that have been reported to be able to interpret EC data automatically one can distinguish use of two methodologies. One is the use of classifiers to assign the signals to several predefined defect classes. Another is the use of expert systems to reason about the shape and other parameters of the signals in order to determine the defect types they represent. Both sorts of systems are usually designed with a specific inspection type in mind (e.g. steam generators of nuclear power plants). Adapting these systems to a different inspection type requires a considerable effort; therefore, they are generally not suitable for application in (petro-) chemical industry where heat-exchanger types vary from one inspection to another. This paper suggests case-based reasoning (CBR) as a methodology which is well suited for s ach applications. In this respect, one of the most important advantages of CBR systems is their ability to leam during use.</p

A Method of Representing and Comparing Eddy Current Lissajous Patterns

In eddy current testing of heat-exchanger pipes the signal of the scanning probe is usually presented in the complex plane as a Lissajous curve. The size (amplitude) of the curve corresponds roughly to the volume of the defect. The phase is related to the depth of the defect and its location (inside or outside defects). Finally, the shape of the curve depends on the form of the defect.</p

Case-based reasoning for interpretation of data from non-destructive testing

Abstract Non-destructive testing (NDT) is a name for a range of methods and procedures used to determine fitness of industrial products for further use. The use of NDT testing techniques results in data in the form of signals, images, or sequences of these, which have to be analysed in order to determine if they contain any indications of defects in the inspected objects. This analysis is often quite complex. In the past, systems have been built which used neural networks (and other statistical classifiers) as well as expert systems to interpret NDT data; however, successful uses of these systems in inspection practice are rare. This article presents how the casebased reasoning methodology (where interpretation of new data is based on previous data-interpretation cases) can be used to tackle the problem of NDT data interpretation. The article presents the characteristics of CBR, which make it an interesting alternative to statistical classifiers and to expert systems. Suitability of CBR for NDT data interpretation is illustrated based on examples of two applications: a CBR system for ultrasonic rail inspection and a CBR system for eddy-current inspection of heat exchangers. r 2001 Published by Elsevier Science Ltd