Rotating Machine Fault Diagnosis Based on Optimal Morphological Filter and Local Tangent Space Alignment
In order to identify the fault of rotating machine effectively, a new method based on the morphological filter optimized by particle swarm optimization algorithm (PSO) and the nonlinear manifold learning algorithm local tangent space alignment (LTSA) is proposed. Firstly, the signal is purified by t...
Saved in:
Main Authors: | Shaojiang Dong, Lili Chen, Baoping Tang, Xiangyang Xu, Zhengyuan Gao, Juan Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2015-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2015/893504 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Plant Leaf Recognition through Local Discriminative Tangent Space Alignment
by: Chuanlei Zhang, et al.
Published: (2016-01-01) -
Approximations of Tangent Polynomials, Tangent –Bernoulli and Tangent – Genocchi Polynomials in terms of Hyperbolic Functions
by: Cristina B. Corcino, et al.
Published: (2021-01-01) -
Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter
by: Yuanqing Luo, et al.
Published: (2019-01-01) -
Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant
by: Xinghui Zhang, et al.
Published: (2015-01-01) -
Identification Procedures as Tools for Fault Diagnosis
of Rotating Machinery
by: Susanne Seibold, et al.
Published: (1995-01-01)