State Monitoring of Lathes Based on Load Current Signal

2012 ◽  
Vol 201-202 ◽  
pp. 758-762
Author(s):  
Yue Ping Yu ◽  
Guang Lin Yu ◽  
Hong Bin Li ◽  
Guo Fu Li
Keyword(s):  
Wavelet Packet ◽  
Wavelet Denoising ◽  
Wavelet Packet Transform ◽  
Current Signal ◽  
State Monitoring ◽  
Denoising Method ◽  
Load Current ◽  
Time Frequency ◽  
Characteristic Quantity ◽  
Motor Load

According to the characteristics of machine tools such as complex driving chain ,weak signal and enclosed housing,this paper takes horizontal lathes as study objects and selects current signal which is easy to sample as the analytical signal.We collect motor load current signals of idling, cylindrical cutting and end cutting processing state in the experiment to process the condition monitoring based on wavelet denoising and wavelet packet transform. We take advantage of the threshold denoising method to reduce noise of load current signal.Then we use time-frequency analysis methods of wavelet packet transform to extract state characteristic quantity and outstand useful information.So in this paper we monitor the working state of lathes based on the unique advantages of wavelet denoising and wavelet packet transform, and this method can be widely used in various fields of state monitoring.

scholarly journals An Integrated Denoising Method for Sensor Mixed Noises Based on Wavelet Packet Transform and Energy-Correlation Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Chao Tan ◽  
Yanping Wang ◽  
Xin Zhou ◽  
Zhongbin Wang ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Processing Method ◽  
Signal Denoising ◽  
Current Signal ◽  
Energy Correlation ◽  
Denoising Method ◽  
Impact Noise ◽  
Key Technologies

In order to solve the problem of industrial sensor signal denoising, an integrated denoising method for sensor mixed noises based on wavelet packet transform and energy-correlation analysis is proposed. The architecture of proposed method is designed and the key technologies, such as wavelet packet transformation, energy-correlation analysis, and processing method of wavelet packet coefficients based on energy-correlation analysis, are presented. Finally, a simulation example for a specific signal and an application of shearer cutting current signal, which mainly contain white Gaussian noise and impact noise, are carried out, and the simulation and application results show that the proposed method is effective and is outperforming others.

Denoising Vibration Signals of Food Refrigerant Air Compressor Based on Wavelet Transform

2010 ◽  
Vol 439-440 ◽  
pp. 1037-1041 ◽  
Author(s):  
Yan Jue Gong ◽  
Zhao Fu ◽  
Hui Yu Xiang ◽  
Li Zhang ◽  
Chun Ling Meng
Keyword(s):  
Wavelet Transform ◽  
Frequency Characteristic ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Denoising Method ◽  
Vibration Signals ◽  
Air Compressor ◽  
Time Frequency ◽  
Soft Threshold

On the basis of wavelet denoising and its better time-frequency characteristic, this paper presents an effective vibration signal denoising method for food refrigerant air compressor. The solution of eliminating strong noise is investigated with the combination of soft threshold and exponential lipschitza. The good denoising results show that the presented method is effective for improving the signal noise ratio and builds the good foundation for further extraction of the vibration signals.

Dual-Tree Wavelet Packet Transform for Ultrasonic Data Transmission Through Metal Structures

2015 ◽  
Author(s):  
Jinjiang Wang ◽  
Robert X. Gao ◽  
Xinyao Tang ◽  
Zhaoyan Fan ◽  
Peng Wang
Keyword(s):  
Signal Processing ◽  
Data Transmission ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Experimental Studies ◽  
Wavelet Packet Transform ◽  
Signal To Noise ◽  
Metallic Structures ◽  
Time Frequency ◽  
Noise Ratio

Data communication through metallic structures is generally encountered in manufacturing equipment and process monitoring and control. This paper presents a signal processing technique for enhancing the signal-to-noise ratio and high-bit data transmission rate in ultrasound-based wireless data transmission through metallic structures. A multi-carrier coded-ultrasonic wave modulation scheme is firstly investigated to achieve high-bit data rate communication while reducing inter-symbol inference and data loss, due to the inherent signal attenuation, wave diffraction and reflection in metallic structures. To improve the signal-to-noise ratio, dual-tree wavelet packet transform (DT-WPT) has been investigated to separate multi-carrier signals under noise contamination, given its properties of shift-invariance and flexible time frequency partitioning. A new envelope extraction and threshold setting strategy for selected wavelet coefficients is then introduced to retrieve the coded digital information. Experimental studies are performed to evaluate the effectiveness of the developed signal processing method for manufacturing.

scholarly journals Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhuo Chen
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Wavelet Denoising ◽  
Simulation Software ◽  
Signal Recognition ◽  
Denoising Method ◽  
Speech Translation ◽  
Time Frequency ◽  
Traditional Algorithm ◽  
Translation Signals

The signal corresponding to English speech contains a lot of redundant information and environmental interference information, which will produce a lot of distortion in the process of English speech translation signal recognition. Based on this, a large number of studies focus on encoding and processing English speech, so as to achieve high-precision speech recognition. The traditional wavelet denoising algorithm plays an obvious role in the recognition of English speech translation signals, which mainly depends on the excellent local time-frequency domain characteristics of the wavelet signal algorithm, but the traditional wavelet signal algorithm is still difficult to select the recognition threshold, and the recognition accuracy is easy to be affected. Based on this, this paper will improve the traditional wavelet denoising algorithm, abandon the single-threshold judgment of the original traditional algorithm, innovatively adopt the combination of soft threshold and hard threshold, further solve the distortion problem of the denoising algorithm in the process of English speech translation signal recognition, improve the signal-to-noise ratio of English speech recognition, and further reduce the root mean square error of the signal. Good noise reduction effect is realized, and the accuracy of speech recognition is improved. In the experiment, the algorithm is compared with the traditional algorithm based on MATLAB simulation software. The simulation results are consistent with the actual theoretical results. At the same time, the algorithm proposed in this paper has obvious advantages in the recognition accuracy of English speech translation signals, which reflects the superiority and practical value of the algorithm.

scholarly journals An Integrated Fault Identification Approach for Rolling Bearings Based on Dual-Tree Complex Wavelet Packet Transform and Generalized Composite Multiscale Amplitude-Aware Permutation Entropy

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Wuqiang Liu ◽  
Xiaoqiang Yang ◽  
Shen Jinxing
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Good Time ◽  
Permutation Entropy ◽  
Fault Identification ◽  
Nonlinear Dimensionality Reduction ◽  
Rolling Bearings ◽  
Time Frequency ◽  
Fault Type ◽  
Complex Wavelet

The health condition of rolling bearings, as a widely used part in rotating machineries, directly influences the working efficiency of the equipment. Consequently, timely detection and judgment of the current working status of the bearing is the key to improving productivity. This paper proposes an integrated fault identification technology for rolling bearings, which contains two parts: the fault predetection and the fault recognition. In the part of fault predetection, the threshold based on amplitude-aware permutation entropy (AAPE) is defined to judge whether the bearing currently has a fault. If there is a fault in the bearing, the fault feature is adequately extracted using the feature extraction method combined with dual-tree complex wavelet packet transform (DTCWPT) and generalized composite multiscale amplitude-aware permutation entropy (GCMAAPE). Firstly, the method decomposes the fault vibration signal into a set of subband components through the DTCWPT with good time-frequency decomposing capability. Secondly, the GCMAAPE values of each subband component are computed to generate the initial candidate feature. Next, a low-dimensional feature sample is established using the t-distributed stochastic neighbor embedding (t-SNE) with good nonlinear dimensionality reduction performance to choose sensitive features from the initial high-dimensional features. Afterwards, the featured specimen representing fault information is fed into the deep belief network (DBN) model to judge the fault type. In the end, the superiority of the proposed solution is verified by analyzing the collected experimental data. Detection and classification experiments indicate that the proposed solution can not only accurately detect whether there is a fault but also effectively determine the fault type of the bearing. Besides, this solution can judge the different faults more accurately compared with other ordinary methods.

Intellectual Gear Fault Detection Based on Wavelet Time-Frequency Analysis

2013 ◽  
Vol 373-375 ◽  
pp. 762-769 ◽  
Author(s):  
Juan Li Zhou
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Gear System ◽  
Support Vector ◽  
Rotating Speed ◽  
Time Frequency ◽  
Fault Recognition ◽  
Vector Machines ◽  
Gear Fault

In this paper, wavelet packet transform and support vector machines are used to detect gear system faults. Testing signals were obtained by measuring the vibration signals of gear system at different rotating speed for different faults. Vibration feature signals were analyzed using wavelet de-noising. By using wavelet packet transform (WPT), signals were decomposed into different frequency bands. the fault detection is used for calculation of energy percents of every frequency. All these were used for fault recognition using Support vector machine (SVM). SVM and neural network transform results were compared. The research indicates that the de-noised signal is superior to the original one. When dealing with various signals, such as Multi-Faults, the diagnosis identification rates are over 92%. This method can be effectively used not only in engineering diagnosis of different faults of gear system, but also for other machinery fault style classification.

A New Adaptive Image Denoising Method Based on Wavelet Packet Transform and Neighbor Dependency

2013 ◽  
Vol 433-435 ◽  
pp. 301-305
Author(s):  
Bin Wen Huang ◽  
Yuan Jiao
Keyword(s):  
High Frequency ◽  
Wavelet Packet ◽  
Estimation Method ◽  
Low Frequency ◽  
Adaptive Threshold ◽  
Difficult Problem ◽  
Wavelet Packet Transform ◽  
Denoising Method ◽  
Image Edges ◽  
Orthogonal Wavelet Transform

In image processing, removal of noise without blurring the image edges is a difficult problem. Aiming at orthogonal wavelet transform and traditional thresholds shortage, a new adaptive threshold image de-noising method which is based on wavelet packet transform and neighbor dependency is proposed. Low frequency part and high frequency part can be decomposed at the same time in wavelet packet transform and the information contained in wavelet coefficients is redundant. Using this kind of relativity in wavelet packet coefficients, we use a new variance neighbor estimation method and then neighbor dependency adaptive threshold is produced. From the experiment result, we see that compared with traditional methods, this method can not only effectively eliminate noise, but can also well keep original images information and the quality after image de-noising is very well.

Condition Feature Extraction of Machine Tools Based on Wavelet Packet Energy Spectrum Analysis and Bispectrum Analysis of Current Signal

2011 ◽  
Vol 101-102 ◽  
pp. 847-850 ◽  
Author(s):  
Teng Fei Fang ◽  
Guo Fu Li
Keyword(s):  
Feature Extraction ◽  
Energy Spectrum ◽  
Real Time ◽  
Spectrum Analysis ◽  
Machine Tools ◽  
Signal Analysis ◽  
Wavelet Packet ◽  
Current Signal ◽  
Load Current ◽  
Working Status

Based on the study of the characteristics of load current signal, this article develops a method to extract features that can be use to distinguish the different working status of machine tools in real-time manner. The features are extracted from wavelet packet energy spectrum and bispectrum of the load current signal, and thus can take advantages of both wavelet packet transforms and bispectrum in signal analysis. Experimental results show that, compared with the features extracted from wavelet packet energy spectrum or bispectrum alone, the features extracted by applying the proposed method can provide better performance in term of identifying the machine working status.

Handwritten Signatures Verification Through Their Acoustic Patterns Based on the Discrete Wavelet-Packet Transform and Semantic-Matching Classifiers

2016 ◽  
Vol 10 (04) ◽  
pp. 557-567 ◽  
Author(s):  
Daniel Angelotti Armiato ◽  
Yuzo Yano ◽  
Vinícius Zani de Faveri ◽  
Rodrigo Capobianco Guido
Keyword(s):  
State Of The Art ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Semantic Matching ◽  
Biometric Authentication ◽  
Discrete Wavelet Packet Transform ◽  
Time Frequency ◽  
Non Invasive ◽  
Hand Shape

Biometric authentication based on fingerprints, voice, hand shape, facial measurements and iris analysis, among others, are quite common nowadays. In a similar manner, the analysis of acoustic patterns generated during the friction between pen and paper at the time a person subscribes has been shown to be a feasible, adequate, and non-invasive alternative to those techniques. An interesting implementation for such an approach, which is described in this paper, is based on the association of the time-frequency analysis supported by the discrete wavelet-packet transform with one of two pattern-matching classifiers, namely Euclidian norma and an original scoring equation derived from correlation, acting semantically. Valuable results were obtained during the tests, motivating further research. The proposed technique is novel on literature, offering a contribution to the state-of-the-art.

Seismic Data Denoising Simulation Research Based on Wavelet Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1356-1360 ◽  
Author(s):  
Shu Cong Liu ◽  
Er Gen Gao ◽  
Chen Xun
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Packet ◽  
Real Data ◽  
Noise Removal ◽  
Wavelet Packet Transform ◽  
Seismic Signals ◽  
Transform Method ◽  
Simulation Research ◽  
Time Frequency

The wavelet packet transform is a new time-frequency analysis method, and is superior to the traditional wavelet transform and Fourier transform, which can finely do time-frequency dividion on seismic data. A series of simulation experiments on analog seismic signals wavelet packet decomposition and reconstruction at different scales were done by combining different noisy seismic signals, in order to achieve noise removal at optimal wavelet decomposition scale. Simulation results and real data experiments showed that the wavelet packet transform method can effectively remove the noise in seismic signals and retain the valid signals, wavelet packet transform denoising is very effective.

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