Next Article in Journal
Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring
Previous Article in Journal
Design of a High-Order Kalman Filter for State and Measurement of A Class of Nonlinear Systems Based on Kronecker Product Augmented Dimension
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 23
Issue 6
10.3390/s23062895
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Axis Orbit Recognition of the Hydropower Unit Based on Feature Combination and Feature Selection

by
Wushuang Liu
,
Yang Zheng
*,
Xuan Zhou
and
Qijuan Chen
School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Sensors 2023, 23(6), 2895; https://doi.org/10.3390/s23062895
Submission received: 27 January 2023 / Revised: 28 February 2023 / Accepted: 4 March 2023 / Published: 7 March 2023
(This article belongs to the Section Intelligent Sensors)
Browse Figures
Versions Notes

Abstract

Axis-orbit recognition is an essential means for the fault diagnosis of hydropower units. An axis-orbit recognition method based on feature combination and feature selection is proposed, aiming to solve the problems of the low recognition accuracy, poor robustness, and low efficiency of existing axis-orbit recognition methods. First, various contour, moment, and geometric features of axis orbit samples are extracted from the original data and combined into a multidimensional feature set; then, Random Forest (RF)-Fisher feature selection is applied to realize feature dimensionality reduction; and finally, the selected features are set as the input of the support vector machine (SVM), which is optimized by the gravitational search algorithm (GSA) for axis-orbit recognition. The analytical results show that the proposed method has high recognition efficiency and good robustness while maintaining high accuracy for axis-orbit recognition.
Keywords: hydropower unit; axis orbit; feature combination; feature selection hydropower unit; axis orbit; feature combination; feature selection

Share and Cite

MDPI and ACS Style

Liu, W.; Zheng, Y.; Zhou, X.; Chen, Q. Axis Orbit Recognition of the Hydropower Unit Based on Feature Combination and Feature Selection. Sensors 2023, 23, 2895. https://doi.org/10.3390/s23062895

AMA Style

Liu W, Zheng Y, Zhou X, Chen Q. Axis Orbit Recognition of the Hydropower Unit Based on Feature Combination and Feature Selection. Sensors. 2023; 23(6):2895. https://doi.org/10.3390/s23062895

Chicago/Turabian Style

Liu, Wushuang, Yang Zheng, Xuan Zhou, and Qijuan Chen. 2023. "Axis Orbit Recognition of the Hydropower Unit Based on Feature Combination and Feature Selection" Sensors 23, no. 6: 2895. https://doi.org/10.3390/s23062895

APA Style

Liu, W., Zheng, Y., Zhou, X., & Chen, Q. (2023). Axis Orbit Recognition of the Hydropower Unit Based on Feature Combination and Feature Selection. Sensors, 23(6), 2895. https://doi.org/10.3390/s23062895

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop