Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings

作者：Luo, Meng; Li, Chaoshun^*; Zhang, Xiaoyuan; Li, Ruhai; An, Xueli

来源：ISA Transactions, 2016, 65: 556-566.

DOI：10.1016/j.isatra.2016.08.022

• Extreme learning machine
• Gravitational search algorithm
• Parameter optimization
• Feature selection
• Ensemble empirical mode decomposition
• Fault diagnosis

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.

• 出版日期2016-11
• 单位中国水利水电科学研究院; 河南工业大学; 华中科技大学