Fault severity recognition based on self-adaptive particle swarm optimisation using wavelet kernel extreme learning machine
Gear fault levels are an important issue and can show the operating status of gears. In this paper, the extreme learning machine (ELM) learning method is regarded as a basic algorithm for classifying gear fault levels. In order to improve the generalisation of the ELM and obtain a better
classification performance, the wavelet kernel concept was introduced to the ELM and thereby a structure called a wavelet kernel extreme learning machine (WK-ELM) was constructed. In terms of the WK-ELM, because the dilation factor of the wavelet kernel and the number of hidden neurons of
the ELM are important for the classification performance, particle swarm optimisation (PSO) was used to search for the most appropriate values of the solved problem. What is more, PSO was also optimised by a self-adaptive inertia weight that formed a novel structure, that is, self-adaptive
inertia weight particle swarm optimisation (SAPSO). So far, an automatic fault diagnosis system for gear fault levels based on the SAPSO-WK-ELM has been proposed in this paper. Aimed at recognising different gear fault levels at different rotational speeds, ranking mutual information (RMI)
and standardised mutual information (SMI) techniques were applied to select sensitive features for gear fault levels. Compared with other relational algorithms previously used, the results of this study, including simulation and experimental data, have demonstrated that the SAPSO-WK-ELM can
achieve an outstanding classification performance.
Keywords: EXTREME LEARNING MACHINE; FEATURE SELECTION; GEAR FAULT DIAGNOSIS; PARTICLE SWARM OPTIMISATION; RANKING MUTUAL INFORMATION; STANDARDISED MUTUAL INFORMATION; WAVELET KERNEL
Document Type: Research Article
Publication date: 01 January 2019
- Official Journal of The British Institute of Non-Destructive Testing - includes original research and development papers, technical and scientific reviews and case studies in the fields of NDT and CM.
- Information for Authors
- Submit a Paper
- Subscribe to this Title
- Information for Advertisers
- Terms & Conditions
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content