文章基本信息

标题：Optimized ELM based on Whale Optimization Algorithm for gearbox diagnosis
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作者：M. Firdaus Isham ; M. Salman Leong ; M. H. Lim 等
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2019
卷号：255
DOI：10.1051/matecconf/201925502003
语种：English
出版社：EDP Sciences
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.