| 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 paper 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% 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 some first 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.
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 paper 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% 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 some first 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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