Genetically programmed-based artificial features extraction applied to fault detection

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Firpi2008558,

• author = "Hiram Firpi and George Vachtsevanos",
• title = "Genetically programmed-based artificial features extraction applied to fault detection",
• journal = "Engineering Applications of Artificial Intelligence",
• volume = "21",
• number = "4",
• pages = "558--568",
• year = "2008",
• ISSN = "0952-1976",
• DOI = "doi:10.1016/j.engappai.2007.06.004",
• URL = "http://www.sciencedirect.com/science/article/B6V2M-4PG2RVD-1/2/83e1929229a124416738c8ec59137146",
• keywords = "genetic algorithms, genetic programming, Fault detection, Feature extraction, Artificial feature, Conventional feature",
• abstract = "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 99percent over training and independent validation sets. As a benchmark, GP-based artificial features constructed from conventional ones under performed those derived from raw data by over 2percent over the training and over 11percent over the testing data.",

}

Genetic Programming entries for Hiram A Firpi George Vachtsevanos

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