Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2017 (2017), Article ID 5963239, 14 pages
Research Article

Casing Vibration Fault Diagnosis Based on Variational Mode Decomposition, Local Linear Embedding, and Support Vector Machine

State Key Lab of Control and Simulation of Power Systems and Generation Equipment, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Yizhou Yang; nc.ude.auhgnist.sliam@61zygnay

Received 10 October 2016; Revised 17 January 2017; Accepted 16 February 2017; Published 12 March 2017

Academic Editor: Dario Di Maio

Copyright © 2017 Yizhou Yang and Dongxiang Jiang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To diagnose mechanical faults of rotor-bearing-casing system by analyzing its casing vibration signal, this paper proposes a training procedure of a fault classifier based on variational mode decomposition (VMD), local linear embedding (LLE), and support vector machine (SVM). VMD is used first to decompose the casing signal into several modes, which are subsignals usually modulated by fault frequencies. Vibrational features are extracted from both VMD subsignals and the original one. LLE is employed here to reduce the dimensionality of these extracted features and make the samples more separable. Then low-dimensional data sets are used to train the multiclass SVM whose accuracy is tested by classifying the test samples. When the parameters of LLE and SVM are well optimized, this proposed method performs well on experimental data, showing its capacity of diagnosing casing vibration faults.