کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5004006 | 1461190 | 2016 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings](/preview/png/5004006.png)
چکیده انگلیسی
This paper proposes a hybrid system named as HGSA-ELM for fault diagnosis of rolling element bearings, in which real-valued gravitational search algorithm (RGSA) is employed to optimize the input weights and bias of ELM, and the binary-valued of GSA (BGSA) is used to select important features from a compound feature set. Three types fault features, namely time and frequency features, energy features and singular value features, are extracted to compose the compound feature set by applying ensemble empirical mode decomposition (EEMD). For fault diagnosis of a typical rolling element bearing system with 56 working condition, comparative experiments were designed to evaluate the proposed method. And results show that HGSA-ELM achieves significant high classification accuracy compared with its original version and methods in literatures.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISA Transactions - Volume 65, November 2016, Pages 556-566
Journal: ISA Transactions - Volume 65, November 2016, Pages 556-566
نویسندگان
Meng Luo, Chaoshun Li, Xiaoyuan Zhang, Ruhai Li, Xueli An,