An Experimental Study on Working State Recognition of Machine Tools

2012 ◽  
Vol 201-202 ◽  
pp. 707-710
Author(s):  
Teng Fei Fang ◽  
Guo Fu Li ◽  
Lei Wang ◽  
Hong Bin Li ◽  
Wei Guo
Keyword(s):  
Frequency Analysis ◽  
Machine Tools ◽  
Wavelet Packet ◽  
Current Signal ◽  
Wavelet Packet Analysis ◽  
Time Frequency Analysis ◽  
State Recognition ◽  
Time Frequency ◽  
Different Characteristics ◽  
One Machine

In order to obtain the real-time working state of machine tools, this experiment extracted the characteristics of machine tools using joint time-frequency analysis and wavelet packet analysis for the total current signal collected, to distinguish which machine is running. First, use joint time-frequency analysis on signal of a single machine to get different characteristics. And find some frequency points with amplitude changing significantly, preparing for the subsequent experiment. Then use wavelet packet analysis on the total signal of more than one machine, finding more obvious characteristics of the different machines with different speeds. Thus it is easy to identify which machine is working. By this experiment, we can save labor, improve efficiency and integrate information in system conveniently.

Current-Aided Time-Frequency Analysis of Vibration Signals for Gearbox Fault Diagnosis

2018 ◽  
Author(s):  
Xiaotong Tu ◽  
Yue Hu ◽  
Fucai Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Current Signal ◽  
Reliable Technique ◽  
Time Frequency Analysis ◽  
Rotating Speed ◽  
Planetary Gearbox ◽  
Time Frequency

Vibration monitoring is an effective method for mechanical fault diagnosis. Wind turbines usually operated under varying-speed condition. Time-frequency analysis (TFA) is a reliable technique to handle such kind of nonstationary signal. In this paper, a new scheme, called current-aided TFA, is proposed to diagnose the planetary gearbox. This new technique acquires necessary information required by TFA from a current signal. The current signal is firstly used to estimate the rotating speed of the shaft. These parameters are applied to the demodulation transform to obtain a rough time-frequency distribution (TFD). Finally, the synchrosqueezing method further enhances the concentration of the obtained TFD. The validation and application of the proposed method are presented by a simulated signal and a vibration signal captured from a test rig.

Improved Hilbert-Huang Transform and its Application in Seismic Data Spectrum Decomposition

2012 ◽  
Vol 152-154 ◽  
pp. 920-923
Author(s):  
Ping Ping Bing ◽  
Si Yuan Cao ◽  
Jiao Tong Lu
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Seismic Data ◽  
Narrow Band ◽  
Numerical Study ◽  
Wavelet Packet ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Spectrum Decomposition

In the conventional seismic data time-frequency analysis, the wavelet transform, wigner ville distribution and so on, cannot meet the high precision time-frequency analysis requirements because of uncertainty principle and cross-term interference. The recently popular Hilbert-Huang transform (HHT) although overcomes these conventional methods’ deficiencies; it still has some unsolved deficiencies due to the theory imperfect. This paper focuses on an improved HHT so as to ameliorate the defect of original HHT. First of all, the wavelet packet transform (WPT) as the preprocessing will be used to the inspected signal, to get some narrow band signals. Then use the empirical mode decomposition (EMD) on the narrow band signals and get the real intrinsic mode function (IMF) by the method of correlation coefficient. From the numerical study and comparison of improved HHT, wavelet transform and HHT, it proves the validity and advantages of this improved method. At last, the improved HHT is applied to marine seismic data by the spectrum decomposition technology, and it well reveals the low frequency shadow phenomenon of the reservoir. The results show that this new method has effectiveness and feasibility in seismic data spectrum decomposition.

scholarly journals A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4965 ◽  
Author(s):  
Shoucong Xiong ◽  
Hongdi Zhou ◽  
Shuai He ◽  
Leilei Zhang ◽  
Qi Xia ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Rolling Bearings ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
End To End

Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.

scholarly journals Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis

2019 ◽  
Vol 19 (4) ◽  
pp. 185-194 ◽  
Author(s):  
Meng-Kun Liu ◽  
Peng-Yi Weng
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Ball Bearing ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Support Vector ◽  
Time Frequency Analysis ◽  
Bearing Faults ◽  
Time Frequency ◽  
Low Efficiency

Abstract Motor-driven machines, such as water pumps, air compressors, and fans, are prone to fatigue failures after long operating hours, resulting in catastrophic breakdown. The failures are preceded by faults under which the machines continue to function, but with low efficiency. Most failures that occur frequently in the motor-driven machines are caused by rolling bearing faults, which could be detected by the noise and vibrations during operation. The incipient faults, however, are difficult to identify because of their low signal-to-noise ratio, vulnerability to external disturbances, and non-stationarity. The conventional Fourier spectrum is insufficient for analyzing the transient and non-stationary signals generated by these faults, and hence a novel approach based on wavelet packet decomposition and support vector machine is proposed to distinguish between various types of bearing faults. By using wavelet and statistical methods to extract the features of bearing faults based on time-frequency analysis, the proposed fault diagnosis procedure could identify ball bearing faults successfully.

The Study of Compactly Supported Orthogonal Two-Direction Vector-Valued Wavelet Packet

2014 ◽  
Vol 919-921 ◽  
pp. 1340-1344
Author(s):  
Lan Li
Keyword(s):  
Functional Analysis ◽  
Frequency Analysis ◽  
Wavelet Packet ◽  
Wavelet Packets ◽  
Direction Vector ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Algebra Theory ◽  
Vector Valued

The concept of two-direction vector-valued wavelet packets is proposed. An approach for constructing orthogonal two-direction vector-valued wavelet packets is developed and their properties are discussed by means of time-frequency analysis method, algebra theory and functional analysis method. Orthogonality formulas concerning these two-direction vector-valued wavelet packets are established.

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