scholarly journals Time-Frequency Feature Extraction Method of the Multi-Source Shock Signal Based on Improved VMD and Bilateral Adaptive Laplace Wavelet

2021 ◽  
Author(s):  
Nanyang Zhao ◽  
Jinjie Zhang ◽  
Zhiwei Mao ◽  
Zhinong Jiang ◽  
He Li
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Economic Losses ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Noise Interference ◽  
Time Frequency ◽  
Fault Recognition ◽  
Penalty Factor ◽  
Frequency Feature

Abstract Reciprocating machinery, e.g., diesel engines and reciprocating compressors, is the key power component in petroleum, petrochemical, nuclear power, and shipbuilding industries. Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery; therefore, it is difficult to extract, analyze, and diagnose mechanical fault features. Moreover, failures occur frequently every year, causing serious economic losses. To accurately and efficiently extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery, a study on the time-frequency feature extraction method of multi-source shock signals was conducted. Combining the characteristics of reciprocating mechanical vibration signals, a targeted optimization method considering the variational modal decomposition (VMD) mode number K and second penalty factor was proposed, which completed the adaptive decomposition of coupled signals. Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals, a new bilateral adaptive Laplace wavelet (BALW) was established. A search strategy for wavelet local parameters of multi-impact signals was proposed using the harmony search (HS) method. A multi-source shock simulation signal was established and actual data of the valve fault were obtained through diesel engine fault experiments. The test results demonstrated that the new method achieved adaptive extraction of local shock features of non-stationary multi-source shock signals and was superior to the original method in terms of signal decomposition effect, sensitive feature extraction, fault recognition accuracy, and parameter search time. The fault recognition rate of the intake and exhaust valve clearance was above 90% and the extraction accuracy of the shock start position was improved by 10°.

scholarly journals Frequency Feature Learning from Vibration Information of GIS for Mechanical Fault Detection

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1949 ◽  
Author(s):  
Yang Yuan ◽  
Suliang Ma ◽  
Jianwen Wu ◽  
Bowen Jia ◽  
Weixin Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Support Vector ◽  
Mechanical State ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Detection Technology ◽  
Intelligent Detection ◽  
Diagnosis Method ◽  
Frequency Feature

The reliability of gas insulated switchgear (GIS) is very important for the safe operation of power systems. However, the research on potential faults of GIS is mainly focused on partial discharge, and the research on the intelligent detection technology of the mechanical state of GIS is very scarce. Based on the abnormal vibration signals generated by a GIS fault, a fault diagnosis method consisting of a frequency feature extraction method based on coherent function (CF) and a multi-layer classifier was developed in this paper. First, the Fourier transform was used to analyze the differences and consistency in the frequency spectrum of signals. Secondly, the frequency domain commonalities of the vibration signals were extracted by using CF, and the vibration characteristics were screened twice by using the correlation threshold and frequency threshold to further select the vibration features for diagnosis. Then, a multi-layer classifier composed of two one-class support vector machines (OCSVMs) and one support vector machine (SVM) was designed to classify the faults of GIS. Finally, the feasibility of the feature extraction method was verified by experiments, and compared with other classification methods, the stability and reliability of the proposed classifier were verified, which indicates that the fault diagnosis method promotes the development of an intelligent detection technology of the mechanical state in GIS.

scholarly journals A New Time–Frequency Feature Extraction Method for Action Detection on Artificial Knee by Fractional Fourier Transform

Micromachines ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 333 ◽  
Author(s):  
Tianrun Wang ◽  
Ning Liu ◽  
Zhong Su ◽  
Chao Li
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Extraction Method ◽  
Fractional Fourier Transform ◽  
Feature Vector ◽  
Action Detection ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Frequency Feature ◽  
New Time

With the aim of designing an action detection method on artificial knee, a new time–frequency feature extraction method was proposed. The inertial data were extracted periodically using the microelectromechanical systems (MEMS) inertial measurement unit (IMU) on the prosthesis, and the features were extracted from the inertial data after fractional Fourier transform (FRFT). Then, a feature vector composed of eight features was constructed. The transformation results of these features after FRFT with different orders were analyzed, and the dimensions of the feature vector were reduced. The classification effects of different features and different orders are analyzed, according to which order and feature of each sub-classifier were designed. Finally, according to the experiment with the prototype, the method proposed above can reduce the requirements of hardware calculation and has a better classification effect. The accuracies of each sub-classifier are 95.05%, 95.38%, 91.43%, and 89.39%, respectively; the precisions are 78.43%, 98.36%, 98.36%, and 93.41%, respectively; and the recalls are 100%, 93.26%, 86.96%, and 86.68%, respectively.

Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

2021 ◽  
Vol 63 (8) ◽  
pp. 465-471
Author(s):  
Shang Zhiwu ◽  
Yu Yan ◽  
Geng Rui ◽  
Gao Maosheng ◽  
Li Wanxiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Extraction Method ◽  
Dynamic Time Warping ◽  
Spectral Feature ◽  
Time Warping ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Dynamic Time

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

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.

scholarly journals Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Liang Hua ◽  
Yujian Qiang ◽  
Juping Gu ◽  
Ling Chen ◽  
Xinsong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Invariant Moment ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Neural Network Algorithm

Automatic extraction of time-frequency spectral image of mechanical faults can be achieved and faults can be identified consequently when rotating machinery spectral image processing technology is applied to fault diagnosis, which is an advantage. Acquired mechanical vibration signals can be converted into color time-frequency spectrum images by the processing of pseudo Wigner-Ville distribution. Then a feature extraction method based on quaternion invariant moment was proposed, combining image processing technology and multiweight neural network technology. The paper adopted quaternion invariant moment feature extraction method and gray level-gradient cooccurrence matrix feature extraction method and combined them with geometric learning algorithm and probabilistic neural network algorithm, respectively, and compared the recognition rates of rolling bearing faults. The experimental results show that the recognition rates of quaternion invariant moment are higher than gray level-gradient cooccurrence matrix in the same recognition method. The recognition rates of geometric learning algorithm are higher than probabilistic neural network algorithm in the same feature extraction method. So the method based on quaternion invariant moment geometric learning and multiweight neural network is superior. What is more, this algorithm has preferable generalization performance under the condition of fewer samples, and it has practical value and acceptation on the field of fault diagnosis for rotating machinery as well.

scholarly journals Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 520
Author(s):  
Tao Liang ◽  
Hao Lu ◽  
Hexu Sun
Keyword(s):  
Feature Extraction ◽  
Empirical Method ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Reconstructed Signal

The decomposition effect of variational mode decomposition (VMD) mainly depends on the choice of decomposition number K and penalty factor α. For the selection of two parameters, the empirical method and single objective optimization method are usually used, but the aforementioned methods often have limitations and cannot achieve the optimal effects. Therefore, a multi-objective multi-island genetic algorithm (MIGA) is proposed to optimize the parameters of VMD and apply it to feature extraction of bearing fault. First, the envelope entropy (Ee) can reflect the sparsity of the signal, and Renyi entropy (Re) can reflect the energy aggregation degree of the time-frequency distribution of the signal. Therefore, Ee and Re are selected as fitness functions, and the optimal solution of VMD parameters is obtained by the MIGA algorithm. Second, the improved VMD algorithm is used to decompose the bearing fault signal, and then two intrinsic mode functions (IMF) with the most fault information are selected by improved kurtosis and Holder coefficient for reconstruction. Finally, the envelope spectrum of the reconstructed signal is analyzed. The analysis of comparative experiments shows that the feature extraction method can extract bearing fault features more accurately, and the fault diagnosis model based on this method has higher accuracy.

scholarly journals An Improved Automatic Shape Feature Extraction Method Based on Template Matching

2021 ◽  
Vol 2095 (1) ◽  
pp. 012053
Author(s):  
Xiaoqi Wang ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Template Matching ◽  
Extraction Method ◽  
Traditional Approach ◽  
Color Space ◽  
Shape Feature ◽  
Feature Extraction Method ◽  
Noise Interference ◽  
Image Shape ◽  
Shape Feature Extraction

Abstract Image shape extraction is an important step in the image analysis, AI electronic industry and automation, as well as a significant part of content-based image retrieval(CBIR), which cannot be separated from contour extraction. However, traditional approach of the border following algorithm is susceptible to noise interference, thus the shape extracted is always complex in real images and cannot express feature of the target image well. Therefore, an improved shape feature extraction method is proposed, which converts color space into HSV model when preprocessing, filters contour by area size, merges adjacent contours by drawing convex hull and filters with template shapes. Lastly, this method is tested on UAV123 and YCB_Video dataset, which showed that the proportion of valid contour improved from less than 10% to 87.7% based on border following algorithm. In the experiment of OPenCV open source library in Visual Studio environment, we hope to improve the extraction efficiency of shape features.

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