A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Chaos and Time-Series Analysis
Chaos and Time-Series Analysis
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
On prediction of epileptic seizures by computing multiple genetic programming artificial features
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Feature generation using genetic programming with application to fault classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox
Expert Systems with Applications: An International Journal
A Review on Fault Diagnosis and Fault Tolerant Control Methods for Single-rotor Aerial Vehicles
Journal of Intelligent and Robotic Systems
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This paper presents a novel application of genetically programmed artificial features, which are computer crafted, data driven, and possibly without physical interpretation, to the problem of fault detection. Artificial features are extracted from vibration data of an accelerometer sensor to monitor and detect a crack fault or incipient failure seeded in an intermediate gearbox of a helicopter's main transmission. Classification accuracies for the artificial feature constructed from raw data exceeded 99% over training and independent validation sets. As a benchmark, GP-based artificial features constructed from conventional ones underperformed those derived from raw data by over 2% over the training and over 11% over the testing data.