Feature extraction of vibration signals based on wavelet packet transform

2004 ◽  
Vol 17 (01) ◽  
pp. 25 ◽  
Author(s):  
Junpeng Shao
Keyword(s):  
Feature Extraction ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Vibration Signals

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.

scholarly journals Feature Analysis of Degradation Signal of Rolling Stock Lateral Damper

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 298
Author(s):  
Zhiyan Zhao ◽  
Bin Wu ◽  
Ting Zhou
Keyword(s):  
Cross Correlation ◽  
Wavelet Packet ◽  
Reference Signal ◽  
Correlation Method ◽  
Health Condition ◽  
Wavelet Packet Transform ◽  
Rolling Stock ◽  
Health State ◽  
Vibration Signals ◽  
Cross Correlation Analysis

The lateral damper is one of the key components of rolling stock. Establishing the relationship between the degraded signal and the health state of the lateral damper is important in order to perform timely performance detection and fault diagnosis. This paper proposes a wavelet packet cross-correlation method (WPCC) that is based on wavelet packet transform (WPT) and cross-correlation analysis (CCA). First, the vibration signals under different running speeds, different running conditions, and different track excitations were collected and analyzed. Second, the wavelet packet transform was used to select larger energy band signals for reconstruction. Subsequently, the WPCC coefficient was calculated between the reference signal and the signal to be measured. The proposed method was applied to analysis of vibration signals of the lateral damper performance degradation. The lateral damper health condition was divided into four intervals, and the average accuracy calculated under different running speeds, different running conditions, and different track excitation was 95%.

Evaluation of Diagnostic Performance for the Gear Box Considering the Measurement Position Effect With Wavelet Packet Transform

2009 ◽  
Author(s):  
Young-Sun Hong ◽  
Gil-Yong Lee ◽  
Young-Man Cho ◽  
Sung-Hoon Ahn ◽  
Chul-Ki Song
Keyword(s):  
Wavelet Packet ◽  
Position Effect ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Wavelet Packet Transform ◽  
Bevel Gear ◽  
Diagnostic Methods ◽  
Vibration Signals ◽  
Rotary Machinery ◽  
Fault Features

There has been much research into monitoring techniques for mechanical systems to ensure stable production levels in modern industries. This is particularly true for the diagnostic monitoring of rotary machinery, because faults in this type of equipment appear frequently and quickly cause severe problems. Such diagnostic methods are often based on the analysis of vibration signals because they are directly related to physical faults. Even though the magnitude of vibration signals depends on the measurement position, the effect of measurement position is generally not considered. This paper describes an investigation of the effect of the measurement position on the fault features in vibration signals. The signals for normal and broken bevel gears were measured at the base, gearbox, and bevel gear, simultaneously, of a machine fault simulator (MFS). These vibration signals were compared to each other and used to estimate the classification efficiency of a diagnostic method using wavelet packet transform. From this experiment, the fault features are more prominently in the vibration signal from the measurement position of the bevel gear than from the base and gearbox. The results of this analysis will assist in selecting the appropriate measurement position in real industrial applications and precision diagnostics.

Energy Distribution Analysis Regarding the Crack Size in a Rotating Shaft

2019 ◽  
Vol 24 (3) ◽  
pp. 418-425
Author(s):  
Cristina Cristina Castejon ◽  
Marıa Jesus Gomez ◽  
Juan Carlos Garcia-Prada ◽  
Eduardo Corral
Keyword(s):  
Wavelet Transform ◽  
Crack Detection ◽  
Wavelet Packet ◽  
Critical Role ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Distribution Analysis ◽  
Rotating Shaft ◽  
Vibration Signals ◽  
Machine Monitoring

Maintenance is critical to avoid catastrophic failures in rotating machinery, and the detection of cracks plays a critical role because they can originate failures with costly processes of reparation, especially in shafts. Vibration signals are widely used in machine monitoring and fault diagnostics. The most critical issue in machine monitoring is the suitable selection of the vibration parameters that represent the condition of the machine. Discrete Wavelet Transform, and one of its recursive forms, called Wavelet Packet Transform, provide a high potential for pattern extraction. Several factors must be selected and taken into account in the Wavelet Transform application such as the level of decomposition, the suitable mother wavelet, and the level basis or features. In this work, the dynamic response of a shaft with different levels of crack is studied. The evolution of energy of the vibration signals obtained from the rotating shaft and the frequencies where maximum increments of energy appear with the crack are analyzed. The results allow the conclusion that changes in energies computed by means of the Wavelet Packet Transform can be successfully used for crack detection.

Fan Fault Feature Extraction Based on Wavelet Packet Transform

2014 ◽  
Vol 668-669 ◽  
pp. 999-1002
Author(s):  
Xin Li ◽  
Pan Feng Guo
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Research Topic ◽  
Signal Characteristic ◽  
Manufacturing Enterprises ◽  
Important Position ◽  
Fault Feature Extraction

Fan occupies the important position in many industry, it give rise to that fault diagnosis become the new hot research topic, also is the urgent demand of many manufacturing enterprises. This paper based on the theory of wavelet packet transform, selecting wavelet packet transform and energy spectrum to wavelet de-noising and fault feature extraction the fan vibration signal. And use the MATLAB get the fan vibration signal characteristic vector, lay the foundation for the fan fault diagnosis.

scholarly journals Comparison of different feature extraction methods for the analysis of uterine magnetomyography signals to predict term labor

2018 ◽  
Vol 7 (3.29) ◽  
pp. 1
Author(s):  
T Ananda Babu ◽  
Dr P. Rajesh Kumar
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Wavelet Packet Transform ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Term Labor

The prediction of term labor by analyzing the uterine magnetomyographic signals attempted in this research. The existing works did not focus on the classification of the signals. Publicly available MIT-BIH database records were divided into term-labor and term-nonlabor groups. This research presents two methods for feature extraction, discrete wavelet transform and wavelet packet transform. Energy, standard deviation, variance, entropy and waveform length of transform coefficients used in the first method. The normalized logarithmic energy of wavelet coefficients from each packet of the total wavelet packet tree used as the feature space for the second method. The labor assessment done through the classification of the features by using five different classifiers for different mother wavelet families. Discrete wavelet transform features extracted using coif5 wavelet with random subspace classification gives the accuracy, precision and FPrates of 93.9286%, 94.2014% and 5.7986% respectively. Using sym8 wavelet for wavelet packet transform features classified with SVM classifier performed well with 95.8763% accuracy, 95.9719% precision and 4.0281% FPrate. The results obtained from the research will be helpful in term labor assessment and understanding the parturition process.  

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