scholarly journals A Gyro Signal Characteristics Analysis Method Based on Empirical Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Qinghua Zeng ◽  
Shanshan Gu ◽  
Jianye Liu ◽  
Sheng Liu ◽  
Weina Chen
Keyword(s):  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Three Dimensional ◽  
Allan Variance ◽  
Analysis Method ◽  
Time Frequency ◽  
Noise Characteristics ◽  
Mode Decomposition ◽  
Signal Characteristics ◽  
Characteristics Analysis

It is difficult to analyze the nonstationary gyro signal in detail for the Allan variance (AV) analysis method. A novel approach in the time-frequency domain for gyro signal characteristics analysis is proposed based on the empirical mode decomposition and Allan variance (EMDAV). The output signal of gyro is decomposed by empirical mode decomposition (EMD) first, and then the decomposed signal is analyzed by AV algorithm. Consequently, the gyro noise characteristics are demonstrated in the time-frequency domain with a three-dimensional (3D) manner. Practical data of fiber optic gyro (FOG) and MEMS gyro are processed by the AV method and the EMDAV algorithm separately. The results indicate that the details of gyro signal characteristics in different frequency bands can be described with the help of EMDAV, and the analysis dimensions are extended compared with the common AV. The proposed EMDAV, as a complementary tool of the AV, which provides a theoretical reference for the gyro signal preprocessing, is a general approach for the analysis and evaluation of gyro performance.

ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD

2009 ◽  
Vol 01 (01) ◽  
pp. 1-41 ◽  
Author(s):  
ZHAOHUA WU ◽  
NORDEN E. HUANG
Keyword(s):  
Data Analysis ◽  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Analysis Method ◽  
True Solution ◽  
New Approach ◽  
Time Frequency ◽  
Time Space ◽  
Mode Decomposition

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturally without any a priori subjective criterion selection as in the intermittence test for the original EMD algorithm. This new approach utilizes the full advantage of the statistical characteristics of white noise to perturb the signal in its true solution neighborhood, and to cancel itself out after serving its purpose; therefore, it represents a substantial improvement over the original EMD and is a truly noise-assisted data analysis (NADA) method.

Application of the Empirical Mode Decomposition Time-Frequency Analysis Method Based on Pipeline Leak Detection

2014 ◽  
Vol 602-605 ◽  
pp. 2213-2216
Author(s):  
Jing Tian
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Traditional Method ◽  
Leak Detection ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Leak Location ◽  
Mode Decomposition ◽  
Decomposition Time

In pipeline leak detection, collected fault signal will inevitably influenced by all kinds of industrial noise, sometimes even the useful signals submerged by the noise, so it causes a huge problem for leak location. Regarding this problem, the Empirical Mode Decomposition was used to analyze the signal instead of the traditional method of Time-Frequency analysis method. Compared with the traditional method, this method can not only get tid of noise, but also analyze the fault signal more accurately, which would help to improve the accuracy of the pipeline leak detection further.

scholarly journals Collaborative Sleep Electroencephalogram Data Analysis Based on Improved Empirical Mode Decomposition and Clustering Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiangwei Zheng ◽  
Xiaochun Yin ◽  
Xuexiao Shao ◽  
Yalin Li ◽  
Xiaomei Yu
Keyword(s):  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Clustering Algorithm ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Stage Classification ◽  
Sleep Stage Classification

Sleep-related diseases seriously affect the life quality of patients. Sleep stage classification (or sleep staging), which studies the human sleep process and classifies the sleep stages, is an important reference to the diagnosis and study of sleep disorders. Many scholars have conducted a series of sleep staging studies, but the correlation between different sleep stages and the accuracy of classification still needs to be improved. Therefore, this paper proposes an automatic sleep stage classification based on EEG. By constructing an improved empirical mode decomposition and K-means experimental model, the concept of “frequency-domain correlation coefficient” is defined. In the process of feature extraction, the feature vector with the best correlation in the time-frequency domain is selected. Extraction and classification of EEG features are realized based on the K-means clustering algorithm. Experimental results demonstrate that the classification accuracy is significantly improved, and our proposed algorithm has a positive impact on sleep staging compared with other algorithms.

A novel bevel gear fault diagnosis method based on ensemble empirical mode decomposition and support vector machines

2020 ◽  
Vol 62 (1) ◽  
pp. 34-41
Author(s):  
Sun Yanqiang ◽  
Chen Hongfang ◽  
Shi Zhaoyao ◽  
Tang Liang
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Bevel Gear ◽  
Support Vector ◽  
Analysis Method ◽  
Mode Decomposition ◽  
Signal Characteristics ◽  
Vector Machines

A novel analysis method is proposed based on ensemble empirical mode decomposition (EEMD) and support vector machines (SVMs) for the fault diagnosis of bevel gears. Firstly, the EEMD method is used to decompose the fluctuations in the original gear noise signals into different timescales so as to obtain several intrinsic mode functions (IMFs). The meshing frequency components in the decomposition results are reconstructed to eliminate the influence of interference noise. Then, time-synchronous averaging (TSA) is applied in further denoising to weaken signals independent of the gear meshing frequency. After denoising, various signal characteristics are calculated. Obvious signal characteristics for different fault states are selected as a set of feature vectors. Finally, a particle optimisation method is used to optimise SVM parameters and the feature vectors are input as training samples into an SVM in order to achieve fault recognition. The experimental results show that this novel analysis method can effectively diagnose different conditions of the bevel gear and achieve an identification rate for gear faults of 98.33%.

scholarly journals Application research on the time–frequency analysis method in the quality detection of ultrasonic wire bonding

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110183
Author(s):  
Wuwei Feng ◽  
Xin Chen ◽  
Cuizhu Wang ◽  
Yuzhou Shi
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Wire Bonding ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Ultrasonic Wire Bonding

Imperfection in a bonding point can affect the quality of an entire integrated circuit. Therefore, a time–frequency analysis method was proposed to detect and identify fault bonds. First, the bonding voltage and current signals were acquired from the ultrasonic generator. Second, with Wigner–Ville distribution and empirical mode decomposition methods, the features of bonding electrical signals were extracted. Then, the principal component analysis method was further used for feature selection. Finally, an artificial neural network was built to recognize and detect the quality of ultrasonic wire bonding. The results showed that the average recognition accuracy of Wigner–Ville distribution and empirical mode decomposition was 78% and 93%, respectively. The recognition accuracy of empirical mode decomposition is obviously higher than that of the Wigner–Ville distribution method. In general, using the time–frequency analysis method to classify and identify the fault bonds improved the quality of the wire-bonding products.

scholarly journals ANALYSIS OF THE PRECIPITATION CLIMATE SIGNAL USING EMPIRICAL MODE DECOMPOSITION (EMD) OVER THE CASPIAN CATCHMENT AREA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 923-929
Author(s):  
F. Sabzehee ◽  
V. Nafisi ◽  
S. Iran Pour ◽  
B. D. Vishwakarma
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Precipitation Time Series ◽  
Climate Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Instantaneous Frequencies ◽  
Better Than

Abstract. In this paper, we employ Empirical Mode Decomposition (EMD) together with Hilbert Transform to analyze precipitation time series over the Caspian Sea catchment. Several studies have shown that EMD can extract nonlinear and non-stationary signals better than Fast Fourier Transform (FFT) and Wavelet Transform. EMD decomposes the time series into a finite number of Intrinsic Mode Functions (IMFs) in the time-frequency domain, while FFT helps us operate either in the time or the frequency domain, which fuels limitations such as the inability of nonstationary signal processing and the lack of time transparency. Although Wavelet Transform is shown to be better than FFT, it fails to detect the instantaneous frequencies and needs to have prior information about characteristics of the data. On the other hand, EMD has shown that it is almost able to determine the signal characteristics with no previous assumptions to estimate the instantaneous frequencies of the signal. In this work, EMD is applied to identify the main frequencies of precipitation time series. Thereafter, a statistical procedure is used to identify the prominent IMF of the original signal.We use the correlation coefficient, Minkowski distance and variance test to extract the relevant and prominent IMFs. The results show that IMF 1–3 are the relevant components and are related to annual and biennial variations of precipitation time series over the Caspian catchment during 2003–2016, respectively.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals Multivariate empirical mode decomposition–based structural damage localization using limited sensors

2021 ◽  
pp. 107754632110069
Author(s):  
Sandeep Sony ◽  
Ayan Sadhu
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Cost Effective ◽  
Threshold Value ◽  
Damage Localization ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Percentage Difference ◽  
Mode Decomposition

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes. A damage localization feature is proposed by calculating the percentage difference in the modal energies of damaged and undamaged structures, followed by the determination of the threshold value of the feature. The feature of the specific sensor location exceeding the threshold value is finally used to identify the location of structural damage. The proposed method is validated using a suite of numerical and full-scale studies. The validation is further explored using various limited measurement cases for evaluating the feasibility of using a fewer number of sensors to enable cost-effective structural health monitoring. The results show the capability of the proposed method in identifying as minimal as 2% change in global modal parameters of structures, outperforming the existing time–frequency methods to delineate such minor global damage.

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