An Improved KPCA-DBN Based Bearing Fault Diagnosis
An Improved KPCA-DBN Based Bearing Fault Diagnosis
- Author(s): T. Hu 1 and N. Lu 1
- DOI: 10.1049/icp.2021.1425
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
Thank you
Your recommendation has been sent to your librarian.
- Author(s): T. Hu 1 and N. Lu 1
-
-
View affiliations
-
Affiliations:
1:
1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics , Nanjing , China
Source:
Jiangsu Annual Conference on Automation (JACA 2020),
2021
p.
45 – 50
-
Affiliations:
1:
1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics , Nanjing , China
- Conference: Jiangsu Annual Conference on Automation (JACA 2020)
- DOI: 10.1049/icp.2021.1425
- ISBN: 978-1-83953-563-5
- Location: Zhenjiang, Jiangsu Province, China
- Conference date: 13-15 November 2020
- Format: PDF
Dreadful hyperparmeters limit the performance of deep learning model. In this paper, we present a novel Kernel Principal Analysis(KPCA)-Deep Belief Network(DBN) hyperparameter optimization approach to diagnose conditions of rolling bearings. The method involves the construction of KPCA-DBN from fuzzy relations, where KPCA is used to reduce the dimension of bearing vibration data. Considering a multi-object optimization problem, Particle Swarm Optimization(PSO) is further performed on the hyperparameter optimization of KPCA-DBN. Experiment results demonstrate that the proposed method could effectively find the optimal hyperparameters.
Inspec keywords: condition monitoring; fault diagnosis; vibrational signal processing; vibrations; deep learning (artificial intelligence); fuzzy set theory; particle swarm optimisation; principal component analysis; rolling bearings; belief networks
Subjects: Vibrations and shock waves (mechanical engineering); Inspection and quality control; Optimisation techniques; Principal component analysis; Mechanical components; Mechanical engineering applications of IT; Combinatorial mathematics; Neural nets; Optimisation; Combinatorial mathematics; Optimisation techniques; Civil and mechanical engineering computing; Principal component analysis; Signal processing and detection; Inspection and quality control; Statistics; Combinatorial mathematics; Maintenance and reliability