id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fumecheng-13292	Li, Xin; Guo, Shiliang; Sun, Dejie; Cao, Lijun; Li, Cong; Tian, Shuyao; Liu, Peng; Qi, Yadong	A ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON EXTREME LEARNING MACHINE OPTIMIZED BY IMPROVED WHALE OPTIMIZATION ALGORITHM	2025	27	.pdf	application/pdf	8900	423	58	The diagnostic results of different fault diagnosis methods for Case 1 and Case 2 are shown in Table 7 and Table 8, respectively. Comparing the results in Tables 7 and 8, the IWOA-ELM model achieved the highest fault identification accuracy for both different types and levels of bearing fault diagnosis Table 7 Diagnosis results of different fault diagnosis methods for Case 1 Model Training accuracy rate Testing accuracy rate IWOA-ELM 98.83% 97.5% WOA-ELM 98.67% 95.5% GWO-ELM 98.33% 95.0% PSO-ELM 98.17% 95.0% ELM 98.17% 90.5% SVM 92.00% 91.25%	cache/fumecheng-13292.pdf	txt/fumecheng-13292.txt