Inductive Logic Programming for Industrial Control Applications

Abstract

Advanced Monitoring Systems of the processes constitute a higher level to the systems of control and use specific techniques and methods. An important part of the task of supervision focuses on the detection and the diagnosis of various situations of faults which can affect the process. Methods of fault detection and diagnosis (FDD) are different from the type of knowledge about the process that they require. They can be classified as data-driven, analytical, or knowledgebased approach. A collaborative FDD approach that combines the strengths of various heterogeneous FDD methods is able to maximize diagnostic performance. The new generation of knowledge-based systems or decision support systems needs to tap into knowledge that is both very broad, but specific to a domain, combining learning, structured representations of domain knowledge such as ontologies and reasoning tools. In this paper, we present a decisionaid tool in case of malfunction of high power industrial steam boiler. For this purpose an ontology was developed and considered as a prior conceptual knowledge in Inductive Logic Programming (ILP) for inducing diagnosis rules. The next step of the process concerns the inclusion of rules acquired by induction in the knowledge base as well as their exploitation for reasoning.