scholarly journals Spacecraft Anomaly Recognition Based on Morphological Variational Mode Decomposition and JRD

2018 ◽  
Vol 36 (1) ◽  
pp. 20-27
Author(s):  
Haixu Jiang ◽  
Ke Zhang ◽  
Jingyu Wang ◽  
Meibo Lü
Keyword(s):  
Measured Data ◽  
Variational Mode Decomposition ◽  
Telemetry Data ◽  
Recognition Method ◽  
State Recognition ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Sample Data ◽  
Orbital Spacecraft ◽  
Weak Anomaly

Considering the difficulty in identifying the in-orbital spacecraft weak anomaly, a spacecraft anomaly state recognition method based on Morphological variational mode decomposition and JRD distance is proposed. First of all, the telemetry data of the spacecraft is decomposed into multi-scale modal functions with different frequencies via morphological variational modal decomposition. Then the Rényi entropy of each modal function is extracted, which is regarded as the feature of telemetry data. Finally, the recognition of spacecraft anomaly state is realized by comparing the JRD distance between the sample data and the measured data. The proposed method is verified by means of the telemetry data of the weak anomaly speed of a satellite reaction wheel. The simulation results demonstrate that the proposed method can effectively identify the anomaly of the spacecraft and has obvious advantage in recognition speed.

scholarly journals Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 24 (2) ◽  
pp. 303-311 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Guowang Zhou ◽  
Dongdong Li ◽  
Haohan Ren
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

scholarly journals A Novel Shearer Cutting State Recognition Method Based on Improved Variational Mode Decomposition and LSSVM with Acoustic Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Zhongbin Wang ◽  
Bin Liang ◽  
Lei Si ◽  
Kuangwei Tong ◽  
Chao Tan
Keyword(s):  
Search Algorithm ◽  
Optimal Parameter ◽  
Gravitational Search Algorithm ◽  
Principal Component ◽  
Least Square ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Recognition Method ◽  
Mode Decomposition

The recognition of shearer cutting state is the key technology to realize the intelligent control of the shearer, which has become a highly difficult subject concerned by the world. This paper takes the sound signal as analytic objects and proposes a novel recognition method based on the combination of variational mode decomposition (VMD), principal component analysis method (PCA), and least square support vector machine (LSSVM). VMD can decompose a signal into various modes by using calculus of variation and effectively avoid the false component and mode mixing problems. On this basis, an improved gravitational search algorithm (IGSA) is designed by using the position update mechanism of Levy flight strategy to find the optimal parameter combination of VMD. Then, the feature extraction is achieved by calculating the envelope entropy and kurtosis of the decomposed intrinsic mode functions (IMFs). To avoid dimensional disasters and reinforce the classification performance, PCA is introduced to choose useful features, and the LSSVM-based classifier is reasonably constructed. Finally, the experimental results indicate that the proposed method is more feasible and superior in the recognition of shearer cutting states.

scholarly journals Arc Fault Detection Algorithm Based on Variational Mode Decomposition and Improved Multi-Scale Fuzzy Entropy

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4137
Author(s):  
Lina Wang ◽  
Hongcheng Qiu ◽  
Pu Yang ◽  
Longhua Mu
Keyword(s):  
Fault Diagnosis ◽  
Detection Algorithm ◽  
Fuzzy Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Experiment Platform ◽  
Multi Scale ◽  
Mode Decomposition

Arc fault diagnosis is necessary for the safety and efficiency of PV stations. This study proposed an arc fault diagnosis algorithm formed by combining variational mode decomposition (VMD), improved multi-scale fuzzy entropy (IMFE), and support vector machine (SVM).. This method first uses VMD to decompose the current into intrinsic mode functions (IMFs) in the time-frequency domain, then calculates the IMFE according to the IMFs associated with the arc fault. Finally, it uses SVM to detect arc faults according to IMFEs. Arc fault data gathered from a PV arc generation experiment platform are used to validate the proposed method. The results indicated the proposed method can classify arc fault data and normal data effectively.

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