Feature Selection on Dynamometer Data for Reliability Analysis

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.