Fault diagnosis for machinery based on feature extraction and general regression neural network

Haiping Li (), Jianmin Zhao, Xianglong Ni and Xinghui Zhang
Additional contact information
Haiping Li: Mechanical Engineering College
Jianmin Zhao: Mechanical Engineering College
Xianglong Ni: Mechanical Engineering College
Xinghui Zhang: Mechanical Engineering College

International Journal of System Assurance Engineering and Management, 2018, vol. 9, issue 5, No 5, 1034-1046

Abstract: Abstract Fault diagnosis for the maintenance of machinery is more difficult since it becomes more precise, automatic and efficient. To tackle this problem, a new feature extraction method for signal processing is developed and a general regression neural network (GRNN)—based method is proposed in this paper. Features are extracted from vibration signals that collected from mechanical systems and a feature selection method based on Euclidean distance technique (EDT) is applied. Then, the selected features are processed by the fault characteristic frequencies of mechanical components. And a part of processed data is as train samples and the others as test samples. Finally, the samples are inputted to GRNN to train and verify the model. The proposed method is applied as a fault diagnosis method for both planetary gearbox and bearings datasets, and the performance of it is validated by compared to such methods as radial basis function neural networks (RBFNN), probabilistic neural network (PNN) and a combination model (EMD–EDT). The experimental results show that the GRNN-based method has an advantage over other similar approaches.

Keywords: Feature extraction; Signal process; Fault diagnosis; General regression neural network (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s13198-018-0726-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:ijsaem:v:9:y:2018:i:5:d:10.1007_s13198-018-0726-9

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-018-0726-9

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().