Optimized ELM based on Whale Optimization Algorithm for gearbox diagnosis

被引:3
|
作者
Isham, M. Firdaus [1 ,2 ]
Leong, M. Salman [1 ]
Lim, M. H. [1 ]
Ahmad, Z. A. B. [2 ]
机构
[1] Univ Teknol Malaysia, Inst Noise & Vibrat, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
[2] Univ Teknol Malaysia, Sekolah Kejuruteraan Mekanikal, Skudai 81310, Johor, Malaysia
关键词
EXTREME LEARNING-MACHINE; BEARING FAULT-DIAGNOSIS;
D O I
10.1051/matecconf/201925502003
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Extreme learning machine (ELM) is a fast and quick learning algorithm with better generalization performance. However, the randomness of input weight and hidden layer bias may affect the overall performance of ELM. This paper proposed a new approach to determine the optimized values of input weight and hidden layer bias for ELM using whale optimization algorithm (WOA), which we call WOA-ELM. An online gearbox vibration signals is used in this study. Empirical mode decomposition (EMD) and complementary mode decomposition (CEEMD) are used to decompose the signals into sub-signals known as intrinsic mode functions (IMFs). Then, statistical features are extracted from selected IMFs. WOA-ELM is used for classification of healthy and faulty condition of gearbox. The result shows that WOA-ELM provide better classification result as compared with conventional ELM. Therefore, this study provide a new diagnosis approach for gearbox application.
引用
收藏
页数:7
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