Automated feature extraction using genetic programming for bearing condition monitoring
Automated feature extraction using genetic programming for bearing condition monitoring
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DOI：
10.1109/mlsp.2004.1423015
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发表时间：
2004-09
期刊：
影响因子：
--
通讯作者：
Hong Guo;Lindsay B. Jack
中科院分区：
文献类型：
--
作者：
Hong Guo;Lindsay B. Jack
特征提取是模式识别的主要挑战之一。这有助于最大限度地利用原始数据中的有用信息，以使分类有效和简单。在本文中，机器学习的方法之一，遗传编程（GP），是从旋转机械的原始振动数据中提取特征，具有几种不同的条件。创建的功能，然后作为输入到一个简单的人工神经网络识别不同的轴承条件，与其他经典的机器学习方法相比。实验结果表明，GP能够自动发现原始振动数据之间的函数关系，从而提高性能
The feature extraction is one of the major challenges for the pattern recognition. This helps to maximise the useful information from the raw data in order to make the classification effective and simple. In this paper, one of the machine learning approaches, genetic programming (GP), is employed to extract features from the raw vibration data taken from a rotating machine with several different conditions. The created features are then used as the input to a simple ANN for the identification of different bearing conditions, in comparison with the other classical machine learning methods. Experimental results demonstrate the capability of GP to discover automatically the functional relationships among the raw vibration data, to give improved performance