Chatter Detection in Milling Process Based on Time-Frequency Analysis

2017 ◽  
Author(s):  
Meng-Kun Liu ◽  
Quang M. Tran ◽  
Yi-Wen Qui ◽  
Chun-Hui Chung
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Milling Process ◽  
Intrinsic Mode Function ◽  
Intrinsic Mode Functions ◽  
Machined Surface ◽  
Time Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
The Rich ◽  
The Stability

Chatter identification is necessary in order to achieve stable machining conditions. However, the linear approximation in regenerative chatter vibration is problematic because of the rich nonlinear characteristics in machining. In this study, a novel method to detect chatter is proposed. Firstly, measured cutting force signals are decomposed into a set of intrinsic mode functions by using ensemble empirical mode decomposition. Hilbert transform is following to extract the instantaneous frequency. Fast Fourier transform is also utilized for each intrinsic mode function to determine the intrinsic mode function that contains rich chatter. Finally, the standard deviation and energy ratio in frequency domain of intrinsic mode functions are found as simply dimensionless chatter indicators. The effectively proposed approach is validated by analyzing the machined surface topography and also compared to the stability lobe diagram.

Approach to extracting gear fault feature based on dominant intrinsic mode function

2014 ◽  
Vol 228 (15) ◽  
pp. 2810-2819 ◽  
Author(s):  
Zhifeng Liu ◽  
Bing Luo ◽  
Wentong Yang ◽  
Ligang Cai ◽  
Jingying Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Function ◽  
Good Time ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Rate Evaluation ◽  
Instantaneous Energy

Complex nonlinear and nonstationary signals can be adaptively analyzed by the Hilbert–Huang transform through empirical mode decomposition and the Hilbert transform to generate the instantaneous energy. The instantaneous energy was able to display the local characteristics of the signals and had good time–frequency analysis capability, it is therefore widely applied to the analysis of vibration signals in the field of gear fault diagnosis. However, only a few extracted intrinsic mode functions through empirical mode decomposition can reflect fault feature or closely related to the faults but others are irrelevant. Therefore, the fault feature of the instantaneous energy for all intrinsic mode functions was not obvious and the accuracy of diagnosis was low. Aimed at solving this problem, a fault leading rate evaluation algorithm was proposed that can select those intrinsic mode functions, which reflect fault features (it was called the dominant intrinsic mode function) from all intrinsic mode functions. In the paper, this algorithm was applied to gear fault feature extraction. By calculating the instantaneous energy of the dominant intrinsic mode function the method could accurately extract gear fault feature and improve the accuracy of diagnosis. Both simulated signals and experimental signals of a Klingelnberg bevel gear were analyzed to verify the effectiveness and correctness of the algorithm.

Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD)

2021 ◽  
Author(s):  
Chun-Hsiang Tang ◽  
Christina W. Tsai
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Time Frequency ◽  
Spatial Locality ◽  
Temperature And Precipitation ◽  
Mode Decomposition ◽  
Climatic Scenarios

<p>Abstract</p><p>Most of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.</p><p>In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.</p><p> </p><p>Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought</p>

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

scholarly journals Feature Extraction of Ship Radiated Noise Based on Permutation Entropy of the Intrinsic Mode Function with the Highest Energy

2016 ◽  
Author(s):  
Yu-Xing Li ◽  
Ya-An Li ◽  
Zhe Chen ◽  
Xiao Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Characteristic Parameter ◽  
Intrinsic Mode Function ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Characteristic Parameters ◽  
Radiated Noise ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

Purification of Axis Trace by Ensemble Empirical Mode Decomposition

2013 ◽  
Vol 791-793 ◽  
pp. 1006-1009
Author(s):  
Jia Xing Zhu ◽  
Wen Bin Zhang ◽  
Ya Song Pu ◽  
Yan Jie Zhou
Keyword(s):  
Prior Knowledge ◽  
Empirical Mode Decomposition ◽  
Simple Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
New Method ◽  
Intrinsic Mode Functions ◽  
Mode Function ◽  
Mode Decomposition ◽  
Novel Method

Aiming at the purification of axis trace, a novel method was proposed by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a collection of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose intrinsic mode function components and reconstructed the signal. Finally the purification of axis trace was obtained. Simulation and practical results show the advantage of ensemble empirical mode decomposition. This method also has simple algorithm and high calculating speed; it provides a new method for purification of axis trace.

scholarly journals A Reservoir Information Extraction Method Based on Improved Hilbert-Huang Transform

2019 ◽  
pp. 85-90
Author(s):  
Qingmi Yang
Keyword(s):  
Signal Processing ◽  
Seismic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Intrinsic Mode Functions ◽  
Signal Processing Technique ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Non Stationary Signal

Hilbert-Huang transform (HHT) is a nonlinear non-stationary signal processing technique, which is more effective than traditional time-frequency analysis methods in complex seismic signal processing. However, this method has problems such as modal aliasing and end effect. The problem causes the accuracy of signal processing to drop. Therefore, this paper introduces the method of combining the Ensemble Empirical Mode Decomposition (EEMD) and the Normalized Hilbert transform (NHT) to extract the instantaneous properties. The specific process is as follows: First, the EEMD method is used to decompose the seismic signal to a series of Intrinsic Mode Functions (IMF), and then The IMFs is screened by using the relevant properties, and finally the NHT is performed on the IMF to obtain the instantaneous properties.

Rolling Bearing Fault Degree Recognition Based on Ensemble Empirical Mode Decomposition and Support Vector Regression

2013 ◽  
Vol 333-335 ◽  
pp. 550-554 ◽  
Author(s):  
Chang Qing Shen ◽  
Fei Hu ◽  
Zhong Kui Zhu ◽  
Fan Rang Kong
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Regression ◽  
Empirical Mode Decomposition ◽  
Economic Loss ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Bearing Fault ◽  
Mode Decomposition

The research in bearing fault diagnosis has been attracting great attention in the past decades. Development of feasible fault diagnosis procedures to prevent failures that could cause huge economic loss timely is necessary. The whole life of the bearing is also a developing process for some sensitive features related to the fault trend. In this paper, a new scheme based on ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) to conduct bearing fault degree recognition is proposed. This analysis first extracts the sensitive features from the intrinsic mode functions (IMFs) produced by EEMD which is a potential time-frequency analysis method, and then constructs an intelligent nonlinear model with input feature vectors extracted from the IMFs and defect size as output. Through validation of experimental data, the results indicated that the bearing fault degree could be effectively and precisely recognized.

scholarly journals Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis

2020 ◽  
Vol 10 (15) ◽  
pp. 5078
Author(s):  
Wenxiao Guo ◽  
Ruiqin Li ◽  
Yanfei Kou ◽  
Jianwei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Fault Simulation ◽  
Random Noise ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Mining Machinery

The feature extraction of composite fault of gearbox in mining machinery has always been a difficulty in the field of fault diagnosis. Especially in strong background noise, the frequency of each fault feature is different, so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. Considering that the signal after complementary ensemble empirical mode decomposition (CEEMD) contains a lot of pseudo components, which further leads to misdiagnosis. The article proposes a new method for actively removing noise components. Firstly, the best scale factor of multi-scale sample entropy (MSE) is determined by signals with different signal to noise ratios (SNRs); secondly, the minimum value of a large number of random noise MSE is extracted and used as the threshold of CEEMD; then, the effective Intrinsic mode functions(IMFs) component is reconstructed, and the reconstructed signal is CEEMD decomposed again; finally, after multiple iterations, the MSE values of the component signal that are less than the threshold are obtained, and the iteration is terminated. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted.

Gearbox Fault Diagnosis Based on Empirical Mode Decomposition and Hilbert Transform

2012 ◽  
Vol 542-543 ◽  
pp. 238-241
Author(s):  
Yan Li Liu ◽  
De Xiang Zhang ◽  
Ming Wei Ji
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Intrinsic Mode Function ◽  
Hilbert Spectrum ◽  
Time Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
Wide Range ◽  
Vibration Signal Analysis

Gearbox is vital components in a wide range of industrial and transport applications. It is very important how to monitor operating state of automobile gearbox and detect incipient faults. This paper applies the empirical mode decomposition (EMD) and Hilbert spectrum methods to gearbox vibration signal analysis capture from vibrating acceleration sensor for gearbox fault diagnosis. The original modulation fault vibration signals are firstly decomposed into a number of intrinsic mode function (IMF) by the EMD method. Then Hilbert spectrum of intrinsic mode function at different fault characteristic frequencies is obtained by Hilbert transform. Finally, the time-frequency fault characteristics of gearbox are analyzed by the Hilbert spectrum value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and Hilbert spectrum characteristic method in fault diagnosis and fault message abstraction.

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