An Effective EMD-Based Feature Extraction Method for Boring Chatter Recognition

2010 ◽  
Vol 34-35 ◽  
pp. 1058-1063 ◽  
Author(s):  
Xin Li ◽  
Zhe He Yao ◽  
Zi Chen Chen
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Chatter Suppression ◽  
Mode Decomposition ◽  
Finished Surface ◽  
Precision Hole

Chatter often occurs during precision hole boring, it results in low quality of finished surface and even damages the cutting tool. In order to identify chatter rapidly and gain the precious time for chatter suppression, a chatter monitoring system was established and an effective feature extraction method for boring chatter recognition was presented. According to the characteristic of chatter signal, empirical mode decomposition (EMD) was introduced into chatter feature extraction, and its basic theories were investigated. The vibration signal was decomposed by EMD, then the intrinsic mode functions (IMF) was got. Finally, the feature of chatter symptom was extracted by analyzing the energy spectrum of each IMF. The results show that feature extracted from vibration of boring bar by EMD can indicate chatter outbreak symptom, and it can be used as feature vectors for rapidly recognizing chatter.

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.

Study on Feature Extraction Method for Diesel Engine Valve Faults

2016 ◽  
Author(s):  
Hongzi Fei ◽  
Long Liu ◽  
Xuemin Li ◽  
Xiuzhen Ma
Keyword(s):  
Feature Extraction ◽  
Diesel Engine ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Stationary Characteristic ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Fault Features ◽  
Empirical Mode Decomposition Method ◽  
Faults Diagnosis

Valve faults diagnosis technique of a diesel engine is studied deeply in this paper. The experiment of valve clearance and air leakage faults are done in a diesel engine, and cylinder head vibration and transient speed signals are measured synchronously on normal and fault conditions respectively. These signals are used to feature extraction. In order to avoid the leakage and aliasing of vibration signal’s frequent spectrum, resample method based on order tracking is proposed, and vibration signal was transformed from time domain to crank angle domain accurately. Considering the non-stationary characteristic of vibration signal, a series of intrinsic mode functions with different scales were obtained using the empirical mode decomposition method, and fault features parameters were extracted through 3D Hilbert spectrums of the intrinsic mode functions. Experimental results show that the method can effectively extract fault features of diesel engine and use them to realize the valve system faults diagnosis further.

scholarly journals Feature Extraction Method for Hidden Information in Audio Streams Based on HM-EMD

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jiu Lou ◽  
Zhongliang Xu ◽  
Decheng Zuo ◽  
Hongwei Liu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Intrinsic Mode Functions ◽  
Hidden Information ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Basic Features ◽  
New Research ◽  
Audio Streams ◽  
The Internet Of Things

Using fake audio to spoof the audio devices in the Internet of Things has become an important problem in modern network security. Aiming at the problem of lack of robust features in fake audio detection, an audio streams’ hidden feature extraction method based on a heuristic mask for empirical mode decomposition (HM-EMD) is proposed in this paper. First, using HM-EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). Then, on the basis of IMFs, basic features and hidden information features HCFs of audio streams are constructed, respectively. Finally, a machine learning method is used to classify audio streams based on these features. The experimental results show that hidden information features of audio streams based on HM-EMD can effectively supplement the nonlinear and nonstationary information that traditional features such as mel cepstrum features cannot express and can better realize the representation of hidden acoustic events, which provide a new research idea for fake audio detection.

scholarly journals A Novel Feature Extraction Method for the Condition Monitoring of Bearings

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2322
Author(s):  
Abdenour Soualhi ◽  
Bilal El Yousfi ◽  
Hubert Razik ◽  
Tianzhen Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Health Indicator ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Spectrum Density ◽  
The Hilbert Transform

This paper presents an innovative approach to the extraction of an indicator for the monitoring of bearing degradation. This approach is based on the principles of the empirical mode decomposition (EMD) and the Hilbert transform (HT). The proposed approach extracts the temporal components of oscillating vibration signals called intrinsic mode functions (IMFs). These components are classified locally from the highest frequencies to the lowest frequencies. By selecting the appropriate components, it is possible to construct a bank of self-adaptive and automatic filters. Combined with the HT, the EMD allows an estimate of the instantaneous frequency of each IMF. A health indicator called the Hilbert marginal spectrum density is then extracted in order to detect and diagnose the degradation of bearings. This approach was validated on two test benches with variable speeds and loads. The obtained results demonstrated the effectiveness of this approach for the monitoring of ball and roller bearings.

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

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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