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Artificial intelligence methods in the diagnostics of analog system

Bilski P.

Prace Naukowe Politechniki Warszawskiej. Elektronika

2013

z. 193

3--178

The topic of the monograph are applications of the artificial intelligence methods to the diagnostics of analog system. The work presents the current stare of the art, introducing the taxonomy of analyzed objects, categorization of existing methods and their applications for the particular tasks. The presented domain is wide, therefore after introducing its main fields, the author’s achievements are presented in detail. The monograph is focused on the data-driven approaches (as opposed to the model-based approaches), which are the standard tool for the data sets analysis and for the decision making. The structure of the data set is explained and methods of the data preprocessing, i. e. discretization, attribute reduction and normalization. These operations are the mandatory step before the intelligent method can be used. The problems of the contemporary diagnostics are introduced. They include ambiguity groups, multiple faults, presence of the additive noise or the tolerances of the system’s parameters. Solving them is crucial for maintaining the system’s testability on the high level. The important part of the work is the overview of the existing diagnostic methods. They were grouped according to their applications and briefly discussed. Approaches used in the experimental examples were introduced in detail. The main categories of intelligent methods include rule-based approaches (rule-induction algorithms, fuzzy logic and rough sets), artificial neural networks (multilayered perceptrons, radial basis functions, support vector machines and other), probabilistic approaches (such as naïve Bayes classifier) and unsupervised learning methods. The optimization algorithms play the auxiliary role to the diagnostic approaches. They facilitate finding the best configuration of the method’s parameters. The analysis of all presented approaches is conducted, considering their advantages, drawbacks and computational complexity. Introduction of existing methods allows for combining them to obtain a better quality of the diagnostic system. The separate chapter is devoted to such advanced solution, for instance combining the rule-induction algorithm with the fuzzy logic or using the unsupervised learning method to detect the ambiguity group. Experimental examples demonstrate possible applications of the classification and regression methods, used to diagnose selected analog system, such as the electrical machine or the electronic circuit. Each example is discussed, revealing the most important features of the presented methods and showing the profits from combining multiple approaches. The presented examples prove that artificial intelligence methods are efficient diagnostic tools, requiring the properly prepared data sets for extracting knowledge and testing it.