DocumentCode :
2652596
Title :
Feature Selection on Dynamometer Data for Reliability Analysis
Author :
Duhaney, Janell ; Khoshgoftaar, Taghi M. ; Sloan, John C.
Author_Institution :
Florida Atlantic Univ., Boca Raton, FL, USA
fYear :
2011
fDate :
7-9 Nov. 2011
Firstpage :
1012
Lastpage :
1019
Abstract :
An ocean turbine extracts the kinetic energy from ocean currents to generate electricity. Vibration signals from the turbine hold a wealth of information regarding its state, and detecting changes in these signals is crucial to the timely detection of faults. Wavelet transforms provide a means of analyzing these complex signals and extracting features which are representative of the signal. Feature selection techniques are needed once these wavelet features are extracted to eliminate redundant or useless features before the data is presented to a machine learning algorithm for pattern recognition and classification. This reduces the quantity of data to be processed and can often even increase the machine learner´s ability to detect the current state of the machine. This paper empirically compares eight feature selection algorithms on wavelet transformed vibration data originating from an onshore test platform for an ocean turbine. A case study shows the classification performances of seven machine learners when trained on the datasets with varying numbers of features selected from the original set of all features. Our results highlight that by choosing an appropriate feature selection technique and applying it to selecting just the 3 most important features (3.33% of the original feature set), some classifiers such as the decision tree and random forest can correctly differentiate between faulty and nonfaulty states almost 100% of the time. These results also show the performance differences between different feature selection algorithms and classifier combinations.
Keywords :
acoustic signal processing; decision trees; dynamometers; feature extraction; learning (artificial intelligence); mechanical engineering computing; pattern classification; random processes; reliability; turbines; vibrations; wavelet transforms; decision tree; dynamometer data; fault detection; feature extraction; feature selection algorithm; kinetic energy; machine learning algorithm; ocean currents; ocean turbine; onshore test platform; pattern classification; pattern recognition; random forest; redundancy elimination; reliability analysis; vibration signal analysis; wavelet transformed vibration data; Feature extraction; Oceans; Sensors; Turbines; Vibrations; Wavelet transforms; classification; condition monitoring; dynamometer; feature selection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Conference_Location :
Boca Raton, FL
ISSN :
1082-3409
Print_ISBN :
978-1-4577-2068-0
Electronic_ISBN :
1082-3409
Type :
conf
DOI :
10.1109/ICTAI.2011.173
Filename :
6103464
Link To Document :
بازگشت