Guo Hong, Jack Lindsay B, Nandi Asoke K
Signal Processing and Communications Group, Department of Electrical Enginerring and Electronics, The University of Liverpool, Liverpool, L69 3GJ, UK.
IEEE Trans Syst Man Cybern B Cybern. 2005 Feb;35(1):89-99. doi: 10.1109/tsmcb.2004.841426.
One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. In this paper, a GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover autimatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results--using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM--have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionaly, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.
模式识别问题中的一个主要挑战是特征提取过程，该过程从现有特征或直接从原始数据中导出新特征，以便在分类过程中降低计算成本，同时提高分类器效率。当前大多数特征提取技术将原始模式向量转换为具有更高判别能力但维度更低的新向量。这是在预定义的特征空间内进行的，因此搜索能力有限。遗传编程（GP）可以在无需概率分布先验知识的情况下从原始数据集中生成新特征。本文提出了一种基于GP的方法，用于从一台在六种不同工况下运行的旋转机械记录的原始振动数据中提取特征。然后将生成的特征用作神经分类器的输入，以识别六种轴承工况。实验结果表明，GP能够利用以非线性函数形式表示的特征自动发现不同的轴承工况。此外，还获得了四组结果——分别是使用GP提取的特征结合人工神经网络（ANN）和支持向量机（SVM），以及传统特征结合ANN和SVM。这种基于GP的方法首次用于轴承故障分类，并且显示出比其他技术更强的搜索能力。此外，与遗传算法（GA）相比，它显著减少了计算时间，因此，使解决方案的实现更加切实可行。
