Study on Fault Diagnosis for Bearing Based on Hierarchical Extreme Learning Machine
| Autor: | Xinan Chen, Limin Jia, Zhipeng Wang, Yakun Zuo, Ning Wang |
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| Rok vydání: | 2020 |
| Předmět: |
Signal processing
Bearing (mechanical) Artificial neural network Computer science 02 engineering and technology Hilbert–Huang transform law.invention Mechanical system Differential entropy law 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Entropy (energy dispersal) Algorithm Extreme learning machine |
| Zdroj: | Lecture Notes in Electrical Engineering ISBN: 9789811528651 |
| DOI: | 10.1007/978-981-15-2866-8_55 |
| Popis: | Rolling bearings are widely used in mechanical systems but have a high damage rate. Its running state is related to the production safety and stable operation of various industries. Nowadays, scholars have applied so many signal processing methods such as differential entropy, energy entropy, and empirical mode decomposition methods in conjunction with various algorithms which likes particle swarms and neural networks to implement pattern classification in the process of the vibration signals of rolling bearings (Qin et al. in Mech Des Manuf 08:11–14, 2018 [1]). On this basis of it, this paper presents the variational mode decomposition–singular value decomposition (VMD-SVD) method based on the previous studies by other scholars with good verification effect that is developed and used to extract the characteristics of different IMF components under different operating conditions in order to establish the characteristic matrix. The latest and better effect of hierarchical extreme learning machine (H-ELM) is applied for training and verification. Besides, by comparing with the traditional ELM method, it verifies its superiority in rolling bearing fault diagnosis. |
| Databáze: | OpenAIRE |
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