A sensor-dependent vibration data driven fault identification method via autonomous variational mode decomposition for transmission system of shipborne antenna

2018 ◽  
Vol 279 ◽  
pp. 376-389 ◽  
Author(s):  
Zipeng Li ◽  
Jinglong Chen ◽  
Yanyang Zi ◽  
Shuilong He
Keyword(s):  
Transmission System ◽  
Data Driven ◽  
Fault Identification ◽  
Variational Mode Decomposition ◽  
Vibration Data ◽  
Identification Method ◽  
Mode Decomposition

scholarly journals The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 359 ◽  
Author(s):  
Yuxing Li ◽  
Xiao Chen ◽  
Jing Yu ◽  
Xiaohui Yang ◽  
Huijun Yang
Keyword(s):  
Feature Extraction ◽  
Optimization Method ◽  
Sample Entropy ◽  
Maximum Energy ◽  
Data Driven ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Radiated Noise ◽  
Mode Decomposition

The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.

scholarly journals Analysis of the sound signal to fault detection of bearings based on Variational Mode Decomposition

2021 ◽  
Vol 942 (1) ◽  
pp. 012020
Author(s):  
Hamid Shiri ◽  
Jacek Wodecki
Keyword(s):  
Fault Detection ◽  
Damage Detection ◽  
Acoustic Signal ◽  
Noise Signal ◽  
Special Treatment ◽  
Variational Mode Decomposition ◽  
Test Rig ◽  
Vibration Signals ◽  
Vibration Data ◽  
Mode Decomposition

Abstract Damage detection in rotating machines is well established for vibration signals. Unfortunately, there are situations, where usage of vibration is not possible. Then, acoustic signal could be used instead. Unfortunately, usually acoustic signal are more noisy and require special treatment for obtain successful damage detection. In the paper we propose to use Variational mode decomposition (VMD) to omit noise for finding de-noise signal. We use vibration data to validate acoustic signal based procedure. The experiment was done on test rig with damaged bearings.

scholarly journals Research on Data-driven Clustering Analysis Fault Identification Method

2020 ◽  
Vol 56 (18) ◽  
pp. 7
Author(s):  
WANG Qingfeng ◽  
LIU Jiahe ◽  
WEI Bingkun ◽  
ZHANG Cheng
Keyword(s):  
Clustering Analysis ◽  
Data Driven ◽  
Fault Identification ◽  
Identification Method

scholarly journals Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tianjing He ◽  
Rongzhen Zhao ◽  
Yaochun Wu ◽  
Chao Yang
Keyword(s):  
Clustering Algorithm ◽  
Optimal Parameter ◽  
Vibration Signal ◽  
Permutation Entropy ◽  
Fault Identification ◽  
Mechanical Equipment ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Reconstructed Signal

The nonlinear and nonstationary characteristics of vibration signal in mechanical equipment make fault identification difficult. To tackle this problem, this paper proposes a novel fault identification method based on improved variational mode decomposition (IVMD), multiscale permutation entropy (MPE), and adaptive GG clustering. Firstly, the original vibration signal is decomposed into a set of mode components adaptively by IVMD, and the mode components that are highly correlated with the original signal are selected to reconstruct the original signal. After that, the MPE values of the reconstructed signal are calculated as feature vectors which can differentiate machinery conditions. Finally, low-dimensional sensitive features obtained by principal component analysis (PCA) are fed into the adaptive GG clustering algorithm to perform fault identification. In this method, the residual energy ratio is used to find the optimal parameter K of the VMD and the PBMF function is incorporated into the GG to determine the number of clusters adaptively. Two bearing datasets are used to validate the performance of the proposed method. The results show that the proposed method can effectively identify different fault types.

scholarly journals The Fault Diagnosis of Rolling Bearing Based on Variational Mode Decomposition and Iterative Random Forest

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Xiwen Qin ◽  
Jiajing Guo ◽  
Xiaogang Dong ◽  
Yu Guo
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Recognition Accuracy ◽  
Rolling Bearing ◽  
Fault Identification ◽  
Variational Mode Decomposition ◽  
Critical Part ◽  
Benchmark Model ◽  
Mode Decomposition ◽  
Proposed Model

Rolling bearing is a critical part of machinery, whose failure will lead to considerable losses and disastrous consequences. Aiming at the research of rotating mechanical bearing data, a fault identification method based on Variational Mode Decomposition (VMD) and Iterative Random Forest (iRF) classifier is proposed. Furthermore, EMD and EEMD are used to decompose the data. At the same time, three mainstream classifiers were selected as the benchmark model. The results show that the proposed model has the highest recognition accuracy.

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