自动特征提取使用遗传编程轴承状态监测

信号处理 Pub Date : 2004-09-29 DOI:10.1109/MLSP.2004.1423015

Hong Guo, L. B. Jack, A. Nandi

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引用次数: 16

摘要

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从原始数据中获取有用信息，从而使分类有效和简单。本文采用遗传规划(GP)作为机器学习的一种方法，从不同条件下的旋转机器的原始振动数据中提取特征。然后将创建的特征用作简单人工神经网络的输入，用于识别不同的轴承条件，并与其他经典机器学习方法进行比较。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，从而提高了性能

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Automated feature extraction using genetic programming for bearing condition monitoring

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

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来源期刊

信号处理

自引率

0.00%

发文量

5812

期刊介绍： Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.