A Method of False Component Discriminant of EMD Based on Kolmogorov-Smirnov Test

2013 ◽  
Vol 427-429 ◽  
pp. 2005-2008
Author(s):  
Wang Can Yang ◽  
Pei Lin Zhang ◽  
Ding Hai Wu ◽  
Zhou Xin
Keyword(s):  
Vibration Signal ◽  
Threshold Value ◽  
Signal Decomposition ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
The Real ◽  
Similarity Threshold ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Smirnov Test

In order to solve the problem that empirical mode decomposition (EMD) will cause false components in the process of signal decomposition, a method of false component discriminant of EMD based on Kolmogorov-Smirnov test was put forward. First, the original signal was decomposed into several intrinsic mode functions (IMFs) by EMD. Then the K-S test was used to calculate the similarity between each IMF and the original signal. The reasonable similarity threshold was selected for judging the authenticity of the IMFs. The IMFs of which the similarity values were less than the threshold value were determined to be the false components. The others of which the similarity values were greater than the threshold value were determined to be the real components. As a result, the false components were removed and the real components were remained. The vibration signal of bearing experiment indicated that the method of K-S test could discriminate the real components and the false components obviously. Then the false components were removed quickly and accurately and the real components of the original signal were obtained.

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

2016 ◽  
Vol 231 (22) ◽  
pp. 4139-4149 ◽  
Author(s):  
Du Wenliao ◽  
Guo Zhiqiang ◽  
Gong Xiaoyun ◽  
Xie Guizhong ◽  
Wang Liangwen ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Detrended Fluctuation ◽  
Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

The improved noise reduction method for the vibration signal based on variational mode decomposition

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 4975-4983
Author(s):  
Zhiting Liu ◽  
Yuhua Wang ◽  
Wenwei Zheng ◽  
Yuexia Zhou
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Reduction Method ◽  
Vibration Signal ◽  
Energy Concentration ◽  
Variational Model ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Model Decomposition ◽  
Mode Decomposition

The variational model decomposition (VMD) has a problem that is dificult to determine the number of intrinsic mode functions (IMF).We use the leaked energy to determine the number of IMFs. And we use the energy concentration rate of the IMF?s autocorrelation function and the correlation coefficient between the IMFs and the original signal, define Q as the ratio of the energy concentration and the correlation coefficient, and use Q to determine the noise IMFs in the IMFs. Then, we filter the noise IMFs and use the remaining IMFs to reconstruct signal to achieve noise reduction. Finally, we use the signal-tonoise ratio (SNR) to compare the noise reduction method proposed in this paper and the Empirical Mode Decomposition (EMD) noise reduction method.

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 Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3510 ◽  
Author(s):  
Zhijian Wang ◽  
Junyuan Wang ◽  
Wenhua Du
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Optimal Number ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Multiple Fault ◽  
Mode Decomposition ◽  
Fault Features ◽  
Reconstructed Signal

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

scholarly journals HHT based analysis on seismic response recordings for a base-isolated building

2019 ◽  
Vol 277 ◽  
pp. 02021
Author(s):  
Fei Wang ◽  
Xiandong Kang ◽  
Ting Yan ◽  
Ying Liu
Keyword(s):  
Seismic Response ◽  
Dominant Frequency ◽  
Data Series ◽  
Structural Systems ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Emd Method

Hilbert-Huang transform (HHT) is proposed to process the seismic response recordings in an 8-story frame-shear wall base-isolated building. Empirical Mode Decomposition (EMD) method is first applied to identify the time variant characteristics and the data series can be decomposed into several components. Hilbert transform is well-behaved in identifying the frequency components. The first 5 intrinsic mode functions (IMFs) are decomposed with their different frequencies. The analytical function is reconstructed and compared with the original signal. They are extremely consistent in amplitude and phase. Based on the IMFs obtained, frequencies of the original signal are inferred at 5 Hz and 1.6 Hz. The higher frequency is regarded as the vibration excited by surface waves. 1.6 Hz is suggested as the dominant frequency of the building. Analysis indicates that HHT is accurate in extracting the dynamic characteristics of structural systems.

A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy

2016 ◽  
Vol 231 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Xueli An ◽  
Junjie Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Real World ◽  
Approximate Entropy ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Simulation Signal

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.

Research on an Algorithm and System for Estimation of Instantaneous Frequency of Rotating Machinery

2012 ◽  
Vol 452-453 ◽  
pp. 153-159
Author(s):  
Rong Qing Yao
Keyword(s):  
Embedded System ◽  
Instantaneous Frequency ◽  
Vibration Signal ◽  
Average Frequency ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
System Technology ◽  
Mode Decomposition ◽  
Phase Signal ◽  
The Embedded System

Instantaneous frequency is an import parameter to diagnose faults of rotating machinery. This paper puts forward an algorithm based Hilbert-Huang Transformation (HHT) to estimate the instantaneous frequency of rotating machinery and develops an instantaneous cymometer based embedded system technology. In order to estimate instantaneous frequency of rotating machinery, the vibration signal is decomposed into a series of intrinsic mode functions (IMF) first by the method of empirical mode decomposition (EMD), then one of the intrinsic mode functions is analyzed with the Hilbert transformation to acquire an estimate value of instantaneous frequency. An instantaneous cymometer is also described in this paper, which is designed to measure the average frequency and instantaneous frequency of rotating machinery in real time. The average frequency is acquired from measuring the cycle of key-phase signal, and the instantaneous frequency is from the above-mentioned method based HHT. The instantaneous cymometer is consisted of an embedded system, which is connected to a PC with an Ethernet. The embedded system is based on an ARM chip (Samsung S3C4510) A/D conversion, EMD and Hilbert transform are completed on the embedded system, and then the results are compressed and sent to the PC by TCP/IP.

scholarly journals Study on Frequency Characteristics of Rotor Systems for Fault Detection Using Variational Mode Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Chen ◽  
Xin-Cong Zhou ◽  
Jun-Qiang Fang ◽  
Li Qin
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Rotor Systems ◽  
Multicomponent Signal ◽  
Mode Decomposition ◽  
Nonlinear Features ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal

Due to the complicated structure, vibration signal of rotating machinery is multicomponent with nonstationary and nonlinear features, so it is difficult to diagnose faults effectively. Therefore, effective extraction of vibration signal characteristics is the key to diagnose the faults of rotating machinery. Mode mixing and illusive components existed in some conventional methods, such as EMD and EEMD, which leads to misdiagnosis in extracting signals. Given these reasons, a new fault diagnosis method, namely, variation mode decomposition (VMD), was proposed in this paper. VMD is a newly developed technique for adaptive signal decomposition, which can decompose a multicomponent signal into a series of quasi-orthogonal intrinsic mode functions (IMFs) simultaneously, corresponding to the components of signal clearly. To further research on VMD method, the advantages and characteristics of VMD are investigated via numerical simulations. VMD is then applied to detect oil whirl and oil whip for rotor systems fault diagnosis via practical vibration signal. The experimental results demonstrate the effectiveness of VMD method.

A NEW APPLICATION OF ENSEMBLE EMD AMELIORATING THE ERROR FROM INSUFFICIENT SAMPLING RATE

2011 ◽  
Vol 03 (04) ◽  
pp. 493-508 ◽  
Author(s):  
DISHAN HUANG ◽  
YULIN XU
Keyword(s):  
Ball Bearing ◽  
Broad Band ◽  
Sampling Rate ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Ensemble Mean ◽  
Mixed Noise ◽  
Bearing Vibration ◽  
The Mean

The objective of this paper is to apply an assisted noise method for ameliorating the empirical mode decomposition (EMD) error from insufficient sampling rate for a vibration signal. When the intrinsic mode functions (IMFs) are extracted from a signal mixed noise at a certain level on the sifting algorithm, an extraordinary phenomenon, where noise submerges the EMD error, is discovered. Thus, noise-assisted data is proposed to disturb the EMD error in the sifting process. In order to cancel out noise after serving its purpose, the IMFs are processed with an ensemble mean. As a result, the noise-assisted data ameliorates the EMD error from insufficient sampling rate, and the method treats the mean as the final true result. An EMD example of ball bearing vibration is presented to illustrate the validity of the approach. This paper recommends implementing the noise-assisted method in the EMD on vibration and acoustic signals with broad band.

Fault Diagnosis for Wind Turbine Gearboxes Based on EMD and the Energy Operator

2013 ◽  
Vol 281 ◽  
pp. 10-13 ◽  
Author(s):  
Xian You Zhong ◽  
Liang Cai Zeng ◽  
Chun Hua Zhao ◽  
Xian Ming Liu ◽  
Shi Jun Chen
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Wind Turbine Gearbox ◽  
Mode Decomposition ◽  
The Cost ◽  
Fault Information

Wind turbine gearbox is subjected to different sorts of failures, which lead to the increasement of the cost. A approach to fault diagnosis of wind turbine gearbox based on empirical mode decomposition (EMD) and teager kaiser energy operator (TKEO) is presented. Firstly, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using EMD. Then the IMF containing fault information is analyzed with TKEO, The experimental results show that EMD and TKEO can be used to effectively diagnose faults of wind turbine gearbox.

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