%0 Journal Article %1 Zhang:2005:MSSP %A Zhang, Liang %A Jack, Lindsay B. %A Nandi, Asoke K. %D 2005 %J Mechanical Systems and Signal Processing %K Condition Fault Feature Roller algorithms, bearing detection, genetic monitoring, programming, selection, %N 2 %P 271--289 %R doi:10.1016/j.ymssp.2004.03.002 %T Fault detection using genetic programming %U http://www.sciencedirect.com/science/article/B6WN1-4CJVC9S-1/2/16e55d1f86d4a8227c9e01e7b37e449d %V 19 %X Genetic programming (GP) is a stochastic process for automatically generating computer programs. GP has been applied to a variety of problems which are too wide to reasonably enumerate. As far as the authors are aware, it has rarely been used in condition monitoring (CM). GP is used to detect faults in rotating machinery. Featuresets from two different machines are used to examine the performance of two-class normal/fault recognition. The results are compared with a few other methods for fault detection: Artificial neural networks (ANNs) have been used in this field for many years, while support vector machines (SVMs) also offer successful solutions. For ANNs and SVMs, genetic algorithms have been used to do feature selection, which is an inherent function of GP. In all cases, the GP demonstrates performance which equals or betters that of the previous best performing approaches on these data sets. The training times are also found to be considerably shorter than the other approaches, whilst the generated classification rules are easy to understand and independently validate.

@article{Zhang:2005:MSSP, abstract = {Genetic programming (GP) is a stochastic process for automatically generating computer programs. GP has been applied to a variety of problems which are too wide to reasonably enumerate. As far as the authors are aware, it has rarely been used in condition monitoring (CM). GP is used to detect faults in rotating machinery. Featuresets from two different machines are used to examine the performance of two-class normal/fault recognition. The results are compared with a few other methods for fault detection: Artificial neural networks (ANNs) have been used in this field for many years, while support vector machines (SVMs) also offer successful solutions. For ANNs and SVMs, genetic algorithms have been used to do feature selection, which is an inherent function of GP. In all cases, the GP demonstrates performance which equals or betters that of the previous best performing approaches on these data sets. The training times are also found to be considerably shorter than the other approaches, whilst the generated classification rules are easy to understand and independently validate.}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Zhang, Liang and Jack, Lindsay B. and Nandi, Asoke K.}, biburl = {https://www.bibsonomy.org/bibtex/24e56d6132b52aa4cf435fab2da273ef7/brazovayeye}, doi = {doi:10.1016/j.ymssp.2004.03.002}, interhash = {35b484f57d75535123b6246dc59570ad}, intrahash = {4e56d6132b52aa4cf435fab2da273ef7}, journal = {Mechanical Systems and Signal Processing}, keywords = {Condition Fault Feature Roller algorithms, bearing detection, genetic monitoring, programming, selection,}, month = {March}, number = 2, owner = {wlangdon}, pages = {271--289}, timestamp = {2008-06-19T17:55:27.000+0200}, title = {Fault detection using genetic programming}, url = {http://www.sciencedirect.com/science/article/B6WN1-4CJVC9S-1/2/16e55d1f86d4a8227c9e01e7b37e449d}, volume = 19, year = 2005 }