Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer
Bearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters...
Saved in:
Main Authors: | Dalian Yang, Jingjing Miao, Fanyu Zhang, Jie Tao, Guangbin Wang, Yiping Shen |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2019-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2019/9303676 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis
by: Jun Shuai, et al.
Published: (2017-01-01) -
Fault Diagnosis of Axle Box Bearing with Acoustic Signal Based on Chirplet Transform and Support Vector Machine
by: Jimin Zhang, et al.
Published: (2022-01-01) -
Fault Detection and Diagnosis in Process Data Using Support Vector Machines
by: Fang Wu, et al.
Published: (2014-01-01) -
An improved SPWM control approach with aid of ant lion optimization for minimizing the THD in multilevel inverters
by: Alaa M. Abdel-hamed, et al.
Published: (2025-01-01) -
Fault Diagnosis of PV Array Based on Time Series and Support Vector Machine
by: Ying Zhong, et al.
Published: (2024-01-01)