Multi-feature learning-based extreme learning machine for rolling bearing fault diagnosis

Longkui Zheng, Yang Xiang and Chenxing Sheng

Journal of Risk and Reliability, 2022, vol. 236, issue 6, 1147-1163

Abstract: Rolling bearing has been becoming an important part of human life and work. The working environment of rolling bearing is very complex and variable, which makes it difficult for fault diagnosis and monitor of rolling bearing from raw vibration data. Then, in this paper, a novel multi-feature learning-based extreme learning machine is proposed for rolling bearing fault diagnosis (FL-ELM). Extreme learning machine (ELM) is a fast and generalized algorithm proposed for training single-hidden-layer feed-forward networks (SLFNs), which has fast computing speed and small testing error. The novel architecture has two hidden layers and an experience pool sandwiched between two hidden layers. The first hidden layer consists of multi-feature learning methods. The experience pool is used to sort and choose new data, with old data being filtered out. Firstly, the first hidden layer is adopted for feature extraction. Secondly, the experience pool is used to rearrange and select data, which is extracted by first hidden layer. Thirdly, ELM is employed to further learn and classify. The proposed method (FL-ELM) is applied to the rolling bearing fault diagnosis. The results confirm that the proposed method is more effective than traditional methods and standard deep learning methods.

Keywords: Multi-feature learning; extreme learning machine; fault diagnosis; experience pool; rolling bearing (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1748006X211048585 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:236:y:2022:i:6:p:1147-1163

DOI: 10.1177/1748006X211048585

Access Statistics for this article

More articles in Journal of Risk and Reliability
Bibliographic data for series maintained by SAGE Publications ().