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Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer
- Source :
- Advances in Mechanical Engineering, Vol 13 (2021)
- Publication Year :
- 2021
- Publisher :
- SAGE Publishing, 2021.
-
Abstract
- Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.
- Subjects :
- 0209 industrial biotechnology
Reducer
Computer science
Mechanical Engineering
Swarm behaviour
Control engineering
02 engineering and technology
Production efficiency
Fault (power engineering)
law.invention
Industrial robot
020901 industrial engineering & automation
law
0202 electrical engineering, electronic engineering, information engineering
TJ1-1570
Robot
020201 artificial intelligence & image processing
Mechanical engineering and machinery
Gradient descent
Extreme learning machine
Subjects
Details
- Language :
- English
- ISSN :
- 16878140
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- Advances in Mechanical Engineering
- Accession number :
- edsair.doi.dedup.....c069bec786de60034b55da2debf0f216