Application of Improved Genetic Programming for Feature Extraction in the Evaluation of Bearing Performance Degradation
Application of Improved Genetic Programming for Feature Extraction in the Evaluation of Bearing Performance Degradation
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改进遗传编程特征提取在轴承性能退化评估中的应用
DOI：
10.1109/access.2020.3019439
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Chen Jin
中科院分区：
文献类型：
--
作者：
Wang Hao;Dong Guangming;Chen Jin
在轴承性能退化的评估中，发现良好的HI（健康指标）是最关键的部分之一，因为它决定了在剩余使用寿命的预测中是否可以获得精确的结果。本文对受生物进化理论启发的启发式迭代搜索算法GP（Genetic Programming）在遗传操作和适应度函数方面进行了改进，并创新性地采用了特征加权矩阵。改进后的GP算法通过融合多个特征来发现HI，具有很好的线性度。通过对发现的HI进行优化，得到了一个适应度更高的优化HI，在RUL预测中可以得到更精确的结果。在2012年IEEE PHM挑战赛提供的轴承全寿命实验数据中验证了所提出的方法，并且在验证中总共使用了三个轴承。
In the evaluation of bearing performance degradation, discovering a good HI (Health Indicator) is one of the most crucial parts, because it determines whether a precise result can be obtained in the prediction of remaining useful life. In this paper, GP (Genetic Programming), which is a heuristic iterative search algorithm inspired by the theory of biological evolution, is improved in genetic operation and fitness function, and a feature weighted matrix is used in GP innovatively. The improved GP is applied to discover a HI by fusing multiple features, which is very close to linearity. Furthermore, by optimizing the discovered HIs, an optimization HI is obtained, which has a higher fitness and can get a more precise result in the prediction of RUL. The proposed approach is verified in the experimental data for the entire life of the bearing provided by 2012 IEEE PHM challenge, and a total of three bearings are used in the verification.
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DOI：
10.1016/s0952-1976(02)00013-1
发表时间：
2001-12
影响因子：
8
作者：
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通讯作者：
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影响因子：
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作者：
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DOI：
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发表时间：
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期刊：
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影响因子：
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