Hidden Markov Mode l Based Tigrigna Speech Recognition
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Date
2009-11
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Addis Ababa University
Abstract
Conventional method of assessing the performance of gas turbine engine involves
analyzing different engine parameters manually and comparing them with their
respective acceptable limits in engines maintenance manuals . The method takes
lengthy and complicated processes that demand personnel with many years of
experience and allows subjective judgment of the personnel involved in evaluation
process. Technological advances in design and constructions of gas turbine engines
adds more eng in e parameters thereby setting more hurdles on the process of
engines performance evaluation .
This paper reports on the finding of a research that had the objective to build a
model that classify the performance, either accepting or rejecting, of PT6A-27 model
turboprop pas turbine engine . The engines cons id erred were those undergo ing
evaluation for performance after they went through repair or overhaul.
The data used to build the mode first passed through different data preprocessing
and analyzing techniques . the model employed neural network built using
back propagation algorithm on a neural network tool box found in MATLAB 6.5 .
The model built classifies gas turbine engines by their performances into their
respective classes . The classification accuracy found was encouraging for the model
to be adapted in real problem. The outcome of this research can also put a corner
stone for further researches in using data mining technique for gas turbine engine
maintenance .
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Information Science