A regular composite feature extraction method for vibration signal pattern recognition in optical fiber link system

This paper presents a feature extraction method based on LMD and MSE for reciprocating compressor according to the strong nonstationarity, nonlinearity and features coupling characteristics of vibration signal. The vibration signal was decomposed into a set of PFs, and then multiscale entropy of the first several PFs were calculated as feature vectors with different scale factors. Based on the maximum of average Euclidean distances, the feature vectors which have the best divisibility were selected. The feature vectors of reciprocating compressor at different bearing clearance states were extracted using this method, and superiority of this method is verified by comparing with the results of sample entropy.

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Study on Feature Extraction Method of Mechanical Vibration Signal for Transformer Winding

Electrical Engineering and Automation ◽

10.1142/9789813220362_0052 ◽

2017 ◽

Author(s):

Xue-bin Li ◽

Tong Luo ◽

Bin Li ◽

Tie Guo ◽

Bin Zhang ◽

...

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Mechanical Vibration ◽

Vibration Signal ◽

Feature Extraction Method ◽

Transformer Winding

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

Entropy ◽

10.3390/e23101319 ◽

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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A Vibration Feature Extraction Method Based on Time-Domain Dimensional Parameters and Mahalanobis Distance

Mathematical Problems in Engineering ◽

10.1155/2021/2498178 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Junjun Chen ◽

Bing Xu ◽

Xin Zhang

Keyword(s):

Feature Extraction ◽

Frequency Domain ◽

Time Domain ◽

Mahalanobis Distance ◽

Extraction Method ◽

Vibration Signal ◽

Feature Extraction Method ◽

Feature Parameters ◽

Dimensional Parameters ◽

Failure Types

To accurately describe the characteristics of a signal, the feature parameters in time domain and frequency domain are usually extracted for characterization. However, the total number of feature parameters in time domain and frequency domain exceeds twenty, and all of the feature parameters are used for feature extraction, which will result in a large amount of data processing. For the purpose of using fewer feature parameters to accurately reflect the characteristics of the vibration signal, a simple but effective vibration feature extraction method combining time-domain dimensional parameters (TDDP) and Mahalanobis distance (MD) is proposed, i.e., TDDP-MD. In this method, ten time-domain dimensional parameters are selected to extract fault features, and the distance evaluation technique based on Mahalanobis distance criterion function is also introduced to calculate the feature vector, which can be used to classify different failure types. Finally, the proposed method is applied to fault diagnosis of rolling element bearings, and experimental analysis results show that the proposed method can recognize different failure types accurately and effectively with only ten time-domain dimensional parameters and a small quantity of training samples.

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Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals

2011 IEEE Conference on Open Systems ◽

10.1109/icos.2011.6079231 ◽

2011 ◽

Author(s):

Nguyen Thi Ngoc Tho ◽

Chandan Kumar Chakrabarty ◽

Yap Keem Siah ◽

Ahmad Basri Abd Ghani

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Feature Extraction ◽

Extraction Method ◽

Partial Discharge ◽

Time Resolved ◽

Feature Extraction Method ◽

Network Pattern

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Feature Extraction Method Based on FDM Energy Entropy and its Application on Optical Fiber Vibration Recognition

Laser & Optoelectronics Progress ◽

10.3788/lop202158.0706006 ◽

2021 ◽

Vol 58 (7) ◽

pp. 0706006

Author(s):

曲洪权 Qu Hongquan ◽

魏冰冰 Wei Bingbing ◽

张正 Zhang Zheng ◽

盛智勇 Sheng Zhiyong

Keyword(s):

Feature Extraction ◽

Optical Fiber ◽

Extraction Method ◽

Feature Extraction Method ◽

Energy Entropy

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Online monitoring and diagnosis of high voltage circuit breaker faults: feature extraction analysis of vibration signals

International Journal of Metrology and Quality Engineering ◽

10.1051/ijmqe/2019012 ◽

2019 ◽

Vol 10 ◽

pp. 13 ◽

Cited By ~ 1

Author(s):

Long Li ◽

Jianfeng Xiao ◽

Bin Wu ◽

Mengge Zhou ◽

Qian Wang

Keyword(s):

Feature Extraction ◽

High Voltage ◽

Extraction Method ◽

Wavelet Packet ◽

Circuit Breaker ◽

Vibration Signal ◽

Normal Operation ◽

Support Vector ◽

Feature Extraction Method ◽

High Voltage Circuit Breaker

The development of power grid system not only increases voltage and capacity, but also increases power risk. This paper briefly introduces the feature extraction method of the vibration signal of high voltage circuit breaker and support vector machine (SVM) algorithm and then analyzed the high voltage circuit breaker in three states: normal operation, fixed screw loosening and falling of opening spring, using the SVM based on the above feature extraction method. The results showed that the accuracy and precision rates of fault identification of circuit breaker were the highest by using the wavelet packet energy entropy extraction features, the false alarm rate was the lowest, and the detection time was the shortest.

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From Complex System Analysis to Pattern Recognition: Experimental Assessment of an Unsupervised Feature Extraction Method Based on the Relevance Index Metrics

Computation ◽

10.3390/computation7030039 ◽

2019 ◽

Vol 7 (3) ◽

pp. 39 ◽

Cited By ~ 4

Author(s):

Laura Sani ◽

Riccardo Pecori ◽

Monica Mordonini ◽

Stefano Cagnoni

Keyword(s):

Pattern Recognition ◽

Feature Extraction ◽

Extraction Method ◽

System Analysis ◽

Feature Space ◽

Extraction Methods ◽

Feature Extraction Method ◽

Real World Datasets ◽

Relevance Index ◽

Unsupervised Feature Extraction

The so-called Relevance Index (RI) metrics are a set of recently-introduced indicators based on information theory principles that can be used to analyze complex systems by detecting the main interacting structures within them. Such structures can be described as subsets of the variables which describe the system status that are strongly statistically correlated with one another and mostly independent of the rest of the system. The goal of the work described in this paper is to apply the same principles to pattern recognition and check whether the RI metrics can also identify, in a high-dimensional feature space, attribute subsets from which it is possible to build new features which can be effectively used for classification. Preliminary results indicating that this is possible have been obtained using the RI metrics in a supervised way, i.e., by separately applying such metrics to homogeneous datasets comprising data instances which all belong to the same class, and iterating the procedure over all possible classes taken into consideration. In this work, we checked whether this would also be possible in a totally unsupervised way, i.e., by considering all data available at the same time, independently of the class to which they belong, under the hypothesis that the peculiarities of the variable sets that the RI metrics can identify correspond to the peculiarities by which data belonging to a certain class are distinguishable from data belonging to different classes. The results we obtained in experiments made with some publicly available real-world datasets show that, especially when coupled to tree-based classifiers, the performance of an RI metrics-based unsupervised feature extraction method can be comparable to or better than other classical supervised or unsupervised feature selection or extraction methods.

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