Multi-fault classification based on the two-stage evolutionary extreme learning machine and improved artificial bee colony algorithm

作者：Xiao Jian; Zhou Jianzhong^*; Li Chaoshun; Xiao Han; Zhang Weibo; Zhu Wenlong

来源：Proceedings of the Institution of Mechanical Engineers - Part C: Journal of Mechanical Engineering Science , 2014, 228(10): 1797-1807.

DOI：10.1177/0954406213496968

• Extreme learning machine
• artificial bee colony
• time-frequency analysis
• rolling element bearing
• fault diagnosis
• evolutionary algorithm

Extreme Learning Machine (ELM) is a novel single-hidden-layer feed forward neural network with fast learning speed and better generalization performance compared with the traditional gradient-based learning algorithms. However, ELM has two issues: the hidden node number of ELM needs to be predefined and the random determination of the input weights and hidden biases lead to ill-condition problem. In this paper, a two-stage evolutionary extreme learning machine (TSE-ELM) algorithm was proposed to overcome the drawbacks of original ELM, which used an improved artificial bee colony (ABC) algorithm to optimize the input weights and hidden biases. The proposed TSE-ELM algorithm was applied on the UCI benchmark datasets and rolling bearing fault diagnosis. The numerical experimental results demonstrated that TSE-ELM had an improved generalization performance than traditional ELM and other evolutionary ELMs.

• 出版日期2014-7
• 单位华中科技大学