Abstract
The extraction and exploitation of existing knowledge assets for supporting decision making and increasing the effectiveness of various internal and external interventions is of critical importance for the success of modern organizations. The use of advanced Operational Research-based quantitative methods in combination with high capabilities information systems can be very useful for this purpose. In this article, we are investigating the use of Ensemble Random Forests for extracting, codifying and exploiting existing organizational knowledge on gas turbine blading faults identification, in the form of a large number of decision trees (called a ‘forest’); each of them has internal nodes corresponding to various tests on features of signals acquired from the gas turbine and leaf nodes corresponding to classifications to the healthy condition or particular faults. Two heterogeneous kinds of inserting randomness to the development of these forest trees, based on different theoretical assumptions, have been examined (Random Input Forests and Random Combination Forests). Using data from a large power gas turbine, the performance of Ensemble Random Forests has been evaluated, and also compared against other machine learning classification methods, such as Neural Networks, Classification and Regression Trees and K-Nearest Neighbor. The Ensemble Random Forests reached a level of 97 per cent in terms of precision and recall in engine condition diagnosis from new signals acquired from the gas turbine, which was higher than the performance of all the other examined classification methods. These results provide first some evidence that Ensemble Random Forest can be an effective tool for the extraction, codification and exploitation of the technological knowledge assets of modern organizations, and contribute significantly to the improvement of organizations' decision making and interventions in this area.
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Notes on contributors
Manolis Maragoudakis
Manolis Maragoudakis holds a PhD from the Department of Electrical and Computer Engineering, University of Patras and a diploma in Computer Science from the Computer Science Department, University of Crete. The thesis was entitled ‘Reasoning under uncertainty in dialogue and other natural language systems using Bayesian network techniques’. He is currently a lecturer in the Department of Information and Communication Systems Engineering at the University of the Aegean, with ‘Data Mining’ as a field of expertise. Maragoudakis is a reviewer for IEEE Transactions on Knowledge and Data Engineering, Knowledge-Based Systems and International Journal of Artificial Intelligence Tools. He has actively supported a plethora of Artificial Intelligence and Data Mining conferences. He is a member of the ‘Ai-Lab’ Group, with the Department of Information and Communication Systems Engineering. Since 2001, he has been a member of the Hellenic Artificial Intelligence Society. He is also a volunteer scientific consultant of the Institute of Marine Conservation Archipelagos. His research interests focus on the following thematic areas: Data Mining, Machine Learning, User Modeling, Bayesian Networks.
Euripides Loukis
Euripidis Loukis is an Assistant Professor of Information Systems and Decision Support Systems in the Department of Information and Communication Systems Engineering, University of the Aegean. Formerly, he has been Information Systems Advisor at the Ministry to the Presidency of the Government of Greece, Technical Director of the Program of Modernization of Greek Public Administration of the Second Community Support Framework and National Representative of Greece in the programs ‘Telematics’ and ‘IDA’ (Interchange of Data between Administrations) of the European Union. He is the author of numerous scientific articles in international journals and conferences; one of them has been honoured with the International Award of the American Society of Mechanical Engineers – Controls and Diagnostics Committee. His current research interests include e-government, information systems value and decision support systems.