scholarly journals Multiscale Entropy Feature Extraction Method of Running Power Equipment Sound

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 685 ◽  
Author(s):  
Yongjie Zhai ◽  
Xu Yang ◽  
Yani Peng ◽  
Xinying Wang ◽  
Kang Bai
Keyword(s):  
Feature Extraction ◽  
Mutual Information ◽  
Extraction Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Power Equipment ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Normalized Mutual Information

The equipment condition monitoring based on computer hearing is a new pattern recognition approach, and the system formed by it has the advantages of noncontact and strong early warning abilities. Extracting effective features from the sound data of the running power equipment help to improve the equipment monitoring accuracy. However, the sound of running equipment often has the characteristics of serious noise, non-linearity and instationary, which makes it difficult to extract features. To solve this problem, a feature extraction method based on the improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and multiscale improved permutation entropy (MIPE) is proposed. Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from the sound of running power equipment. The noise IMFs are then identified and eliminated through mutual information (MI) and mean mutual information (meanMI) of IMFs. Next, the normalized mutual information (norMI) and MIPE are calculated respectively, and norMI is utilized to weigh the corresponding MIPE result. Finally, based on the separability criterion, the weighted MIPE results are feature-dimensionally reduced to obtain the multiscale entropy feature of the sound. The experimental results show that the classification accuracies of the method under the conditions of no noise and 5 dB reach 96.7% and 89.9%, respectively. In practice, the proposed method has higher reliability and stability for the sound feature extraction of the running power equipment.

scholarly journals A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 624 ◽  
Author(s):  
Zhe Chen ◽  
Yaan Li ◽  
Renjie Cao ◽  
Wasiq Ali ◽  
Jing Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Mutual Information ◽  
Noise Reduction ◽  
Extraction Method ◽  
Recognition Rate ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Normalized Mutual Information ◽  
Radiated Noise ◽  
New Feature

Extracting useful features from ship-radiated noise can improve the performance of passive sonar. The entropy feature is an important supplement to existing technologies for ship classification. However, the existing entropy feature extraction methods for ship-radiated noise are less reliable under noisy conditions because they lack noise reduction procedures or are single-scale based. In order to simultaneously solve these problems, a new feature extraction method is proposed based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), normalized mutual information (norMI), and multiscale improved permutation entropy (MIPE). Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from ship-radiated noise. The noise reduction process is then conducted by identifying and eliminating the noise IMFs. Next, the norMI and MIPE of the signal-dominant IMFs are calculated, respectively; and the norMI is used to weigh the corresponding MIPE result. The multi-scale entropy feature is finally defined as the sum of the weighted MIPE results. Experimental results show that the recognition rate of the proposed method achieves 90.67% and 83%, respectively, under noise free and 5 dB conditions, which is much higher than existing entropy feature extraction algorithms. Hence, the proposed method is more reliable and suitable for feature extraction of ship-radiated noise in practice.

A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine

2016 ◽  
Vol 231 (6) ◽  
pp. 1146-1161 ◽  
Author(s):  
Pak Kin Wong ◽  
Jian-Hua Zhong ◽  
Zhi-Xin Yang ◽  
Chi Man Vong
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Extraction Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Committee Machine ◽  
Feature Extraction Method ◽  
Diagnostic Framework ◽  
The Individual ◽  
New Framework

This paper proposes a new diagnostic framework, namely, probabilistic committee machine, to diagnose simultaneous-fault in the rotating machinery. The new framework combines a feature extraction method with ensemble empirical mode decomposition and singular value decomposition, multiple pairwise-coupled sparse Bayesian extreme learning machines (PCSBELM), and a parameter optimization algorithm to create an intelligent diagnostic framework. The feature extraction method is employed to find the features of single faults in a simultaneous-fault pattern. Multiple PCSBELM networks are built as different signal committee members, and each member is trained using vibration or sound signals respectively. The individual diagnostic result from each fault detection member is then combined by a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable fault as compared to individual classifier acting alone. The effectiveness of the proposed framework is verified by a case study on a gearbox fault detection. Experimental results show the proposed framework is superior to the existing single probabilistic classifier. Moreover, the proposed system can diagnose both single- and simultaneous-faults for the rotating machinery while the framework is trained by single-fault patterns only.

A Fault Feature Extraction Method Based on LMD and MSE for Reciprocating Compressor

2014 ◽  
Vol 530-531 ◽  
pp. 345-348
Author(s):  
Min Qiang Xu ◽  
Hai Yang Zhao ◽  
Jin Dong Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Vibration Signal ◽  
Multiscale Entropy ◽  
Reciprocating Compressor ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Euclidean Distances ◽  
Coupling Characteristics ◽  
Fault Feature Extraction

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.

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.

An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

2019 ◽  
Vol 26 (3-4) ◽  
pp. 146-160
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yongbo Li ◽  
Xiaoqiang Du
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Extraction Method ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Multichannel Signals ◽  
Fault Feature Extraction ◽  
Fault Information

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

Research on Vibration Signal Feature Extraction Method to the Wind Turbine Generator

2014 ◽  
Vol 902 ◽  
pp. 370-377
Author(s):  
Guo Xin Wu ◽  
Yun Bo Zuo ◽  
Yan Hui Shi
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Power ◽  
Extraction Method ◽  
Vibration Signal ◽  
Power Equipment ◽  
Feature Extraction Method ◽  
Current State ◽  
Diagnosis Method

Aiming at the safe operation of the wind turbine, a feature extraction method of vibration signal based on the principle of blind source separation was proposed. Blind source and the current state of fault signal was separated and predicated by Using historical data of wind turbine vibration signal as the observation noise, and then fault diagnosis signal mechanical operation was analyzed, the HMM/SVM series fault diagnosis models structure was proposed. By calculating the matching degree of unknown signal and wind power equipment in the state using HMM, the features for SVM was formed to achieve the finally discriminant. The experimental results showed that the fault diagnosis method can precisely and effectively complete the wind power equipment, higher than pure HMM or SVM diagnostic accuracy.

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