A Novel Feature Extraction Method Using Ant Colony Clustering Analysis

2010 ◽  
Vol 37-38 ◽  
pp. 32-35
Author(s):  
De Bin Zhao ◽  
Ji Hong Yan
Keyword(s):  
Feature Extraction ◽  
Clustering Analysis ◽  
Extraction Method ◽  
Ant Colony Algorithm ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Ant Colony ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Ant Colony Clustering

A novel feature extraction method is presented by combining wavelet packet transform with ant colony clustering analysis in this paper. Vibration signals acquired from equipments are decomposed by wavelet packet transform, after which frequency bands of signals are clustered by ant colony algorithm, and each cluster as a set of data is analyzed in frequency-domain for extracting intrinsic features reflecting operating condition of machinery. Furthermore, the robust ant colony clustering algorithm is proposed by adjusting comparing probability dynamically. Finally, effectiveness and feasibility of the proposed method are verified by vibration signals acquired from a rotor test bed.

Research on the NPC Three-Level Inverter Fault Feature Extraction Method Based on Wavelet Analysis

2014 ◽  
Vol 494-495 ◽  
pp. 1410-1413
Author(s):  
Rong Hua ◽  
Ping Ping Ji
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Wavelet Packet ◽  
Extraction Procedure ◽  
Wavelet Packet Transform ◽  
Matlab Simulation ◽  
Feature Extraction Method ◽  
Extraction Algorithm ◽  
Fault Feature Extraction ◽  
Fault Information

This paper study a fault feature extraction method based on wavelet packet transform, using this method to extract fault information of output signal in the NPC three-level inverter fault. After analysis the structure principle of NPC three-level inverter and the wavelet packets fault feature extraction algorithm, brings up a fault feature extraction procedure of NPC three-level inverter based on wavelet packet transform. Through MATLAB simulation experiments, prove the rationality of wavelet packet transform in fault feature extraction, and the reliability in intelligent fault diagnosis.

Hot Strip Tail Flick Recognition Based on Morphological Pattern Spectrum

2012 ◽  
Vol 572 ◽  
pp. 25-30
Author(s):  
Li Jing Han ◽  
Jian Hong Yang ◽  
Min Lin ◽  
Jin Wu Xu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Steel Strip ◽  
Wavelet Packet ◽  
Support Vector ◽  
Tail Flick ◽  
Morphological Pattern ◽  
Feature Extraction Method ◽  
Pattern Spectrum ◽  
Hot Strip

Hot strip tail flick is an abnormal production phenomenon, which brings many damages. To recognize the tail flick signals from all throwing steel strip signals, a feature extraction method based on morphological pattern spectrum is proposed in this paper. The area between signal curves after multiscale opening operation and the horizontal axis is computed as the pattern spectrum value and it reflects the geometric information differences. Then, support vector machine is used as the classifier. Experimental results show that the total correct rate based on pattern spectrum feature reached 96.5%. Compared with wavelet packet energy feature, the total correct rate is 92.1%. So, the feasibility and availability of this new feature extraction method are verified.

scholarly journals A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1319
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Yucai Li ◽  
Wei Lin ◽  
Jinjuan Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Vibration Signal ◽  
Practical Significance ◽  
Stable Operation ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Multi Scale

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

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