A Method for Predicting the Remaining Useful Life of Bearings Based on Genetic Programming

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@InProceedings{Wang:2022:IMCEC,

• author = "Guan Wang and Shaoliang Hu and Bowen Feng and Huimin Liu and Zhibing Zhu",
• booktitle = "2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)",
• title = "A Method for Predicting the Remaining Useful Life of Bearings Based on Genetic Programming",
• year = "2022",
• volume = "5",
• pages = "1592--1596",
• abstract = "In the remaining life prediction of bearings, feature extraction is crucial because it directly determines the prediction accuracy of the model. In response to this problem, this paper proposes a feature extraction method based on genetic programming. First, the multi-dimensional features are combined into an independent feature combination in the form of a feature tree, and then an improved fitness function is designed. After many iterations, The feature combination with the highest fitness is finally output, which is called the optimization feature. Finally, the least square method is used to predict the optimization characteristic curve, and the life prediction can be carried out by combining with the failure model. Finally, the public data set of bearing full life is used to predict the remaining service life of the bearing with the optimization feature as the model, which verifies the accuracy of the algorithm prediction.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/IMCEC55388.2022.10019961",
• ISSN = "2693-2776",
• month = dec,
• notes = "Also known as \cite{10019961}",

}

Genetic Programming entries for Guan Wang Shaoliang Hu Bowen Feng Huimin Liu Zhibing Zhu

Citations