scholarly journals Multi-Fault Diagnosis of Gearbox Based on Improved Multipoint Optimal Minimum Entropy Deconvolution

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 611 ◽  
Author(s):  
Fuhe Yang ◽  
Xingquan Shen ◽  
Zhijian Wang
Keyword(s):  
White Noise ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Minimum Entropy ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Fault Features ◽  
Selection Of

Under complicated conditions, the extraction of a multi-fault in gearboxes is difficult to achieve. Due to improper selection of methods, leakage diagnosis or misdiagnosis will usually occur. Ensemble Empirical Mode Decomposition (EEMD) often causes energy leakage due to improper selection of white noise during signal decomposition. Considering that only a single fault cycle can be extracted when MOMED (Multipoint Optimal Minimum Entropy Deconvolution) is used, it is necessary to perform the sub-band processing of the compound fault signal. This paper presents an adaptive gearbox multi-fault-feature extraction method based on Improved MOMED (IMOMED). Firstly, EEMD decomposes the signal adaptively and selects the intrinsic mode functions with strong correlation with the original signal to perform FFT (Fast Fourier transform); considering the mode-mixing phenomenon of EEMD, reconstruct the intrinsic mode functions with the same timescale, and obtain several intrinsic mode functions of the same scale to improve the entropy of fault features. There is a lot of white noise in the original signal, and EEMD can improve the signal-to-noise ratio of the original signal. Finally, through the setting of different noise-reduction intervals to extract fault features through MOMED. The proposed method is compared with EEMD and VMD (Variational Mode Decomposition) to verify its feasibility.

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.

A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

2014 ◽  
Vol 548-549 ◽  
pp. 369-373
Author(s):  
Yuan Cheng Shi ◽  
Yong Ying Jiang ◽  
Hai Feng Gao ◽  
Jia Wei Xiang
Keyword(s):  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Bearing Faults ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Inner Race

The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.

New Feature Extraction Method for the Detection of Defects in Rolling Element Bearings

2012 ◽  
Vol 134 (8) ◽  
Author(s):  
Ling Xiang ◽  
Aijun Hu
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Outer Race ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Envelope Spectrum ◽  
Detection Of Defects

This paper proposes a new method based on ensemble empirical mode decomposition (EEMD) and kurtosis criterion for the detection of defects in rolling element bearings. Some intrinsic mode functions (IMFs) are presented to obtain symptom wave by EEMD. The different kurtosis of the intrinsic mode function is determined to select the envelope spectrum. The fault feature based on the IMF envelope spectrum whose kurtosis is the maximum is extracted, and fault patterns of roller bearings can be effectively differentiated. Practical examples of diagnosis for a rolling element bearing are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race and inner-race, can be effectively identified by the proposed method.

scholarly journals The Measurement and Elimination of Mode Splitting: From the Perspective of the Partly Ensemble Empirical Mode Decomposition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Liu ◽  
Peng Zheng ◽  
Qilin Dai ◽  
Zhongli Zhou
Keyword(s):  
Spectral Analysis ◽  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Mode Splitting ◽  
Mode Decomposition ◽  
Microseismic Signal ◽  
Mode Mixing

The problems of mode mixing, mode splitting, and pseudocomponents caused by intermittence or white noise signals during empirical mode decomposition (EMD) are difficult to resolve. The partly ensemble EMD (PEEMD) method is introduced first. The PEEMD method can eliminate mode mixing via the permutation entropy (PE) of the intrinsic mode functions (IMFs). Then, bilateral permutation entropy (BPE) of the IMFs is proposed as a means to detect and eliminate mode splitting by means of the reconstructed signals in the PEEMD. Moreover, known ingredient component signals are comparatively designed to verify that the PEEMD method can effectively detect and progressively address the problem of mode splitting to some degree and generate IMFs with better performance. The microseismic signal is applied to prove, by means of spectral analysis, that this method is effective.

Study on Feature Extraction Method for Diesel Engine Valve Faults

2016 ◽  
Author(s):  
Hongzi Fei ◽  
Long Liu ◽  
Xuemin Li ◽  
Xiuzhen Ma
Keyword(s):  
Feature Extraction ◽  
Diesel Engine ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Stationary Characteristic ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Fault Features ◽  
Empirical Mode Decomposition Method ◽  
Faults Diagnosis

Valve faults diagnosis technique of a diesel engine is studied deeply in this paper. The experiment of valve clearance and air leakage faults are done in a diesel engine, and cylinder head vibration and transient speed signals are measured synchronously on normal and fault conditions respectively. These signals are used to feature extraction. In order to avoid the leakage and aliasing of vibration signal’s frequent spectrum, resample method based on order tracking is proposed, and vibration signal was transformed from time domain to crank angle domain accurately. Considering the non-stationary characteristic of vibration signal, a series of intrinsic mode functions with different scales were obtained using the empirical mode decomposition method, and fault features parameters were extracted through 3D Hilbert spectrums of the intrinsic mode functions. Experimental results show that the method can effectively extract fault features of diesel engine and use them to realize the valve system faults diagnosis further.

scholarly journals Seismic Random Noise Attenuation Method Based on Variational Mode Decomposition and Correlation Coefficients

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 280 ◽  
Author(s):  
Yaping Huang ◽  
Hanyong Bao ◽  
Xuemei Qi
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Noise Attenuation ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Random Noise Attenuation ◽  
Mode Decomposition

Seismic data is easily affected by random noise during field data acquisition. Therefore, random noise attenuation plays an important role in seismic data processing and interpretation. According to decomposition characteristics of seismic signals by using variational mode decomposition (VMD) and the constraint conditions of correlation coefficients, this paper puts forward a method for random noise attenuation in seismic data, which is called variational mode decomposition correlation coefficients VMDC. Firstly, the original signals were decomposed into intrinsic mode functions (IMFs) with different characteristics by VMD. Then, the correlation coefficients between each IMF and the original signal were calculated. Next, based on the differences among correlation coefficients of effective signals and random noise as well as the original signals, the corresponding treatment was carried out, and the effective signals were reconstructed. Finally, the random noise attenuation was realized. After adding random noise to simple sine signals and the synthetic seismic record, the improved complementary ensemble empirical mode decomposition (ICEEMD) and VMDC were used for testing. The testing results indicate that the proposed VMDC has better random noise attenuation effects. It was also used in real-world seismic data noise attenuation. The results also show that it could effectively improve the signal-to-noise ratio (SNR) of seismic data and could provide high-quality basic data for further interpretation of seismic data.

scholarly journals Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Fengtao Wang ◽  
Chenxi Liu ◽  
Wensheng Su ◽  
Zhigang Xue ◽  
Qingkai Han ◽  
...  
Keyword(s):  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Heavy Load ◽  
Heavy Equipment ◽  
Mode Decomposition ◽  
Fault Features ◽  
Slewing Bearings ◽  
Combined Failure

Large-size and heavy-load slewing bearings, which are mainly used in heavy equipment, comprise a subgroup of rolling bearings. Owing to the complexity of the structures and working conditions, it is quite challenging to effectively diagnose the combined failure and extract fault features of slewing bearings. In this study, a method was proposed to denoise and classify the combined failure of slewing bearings. First, after removing the mean, the vibration signals were denoised by maximum correlated kurtosis deconvolution. The signals were then decomposed into several intrinsic mode functions (IMFs) by complementary ensemble empirical mode decomposition (CEEMD). Appropriate IMFs were selected based on the correlation coefficient and kurtosis. The approximate entropy values of the selected IMFs were regarded as the characteristic vectors and then inputted into the support vector machine (SVM) based on multiclass classification for training. The practical combined failure signals of the 3 conditions were finally recognized and classified using SVMs. The study also compared the proposed method with 5 other methods to demonstrate the superiority and effectiveness of the proposed method.

scholarly journals A New Ship-Radiated Noise Feature Extraction Technique Based on Variational Mode Decomposition and Fluctuation-Based Dispersion Entropy

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 235 ◽  
Author(s):  
Hong Yang ◽  
Ke Zhao ◽  
Guohui Li
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Mode Decomposition ◽  
Underwater Acoustic Channels ◽  
The Difference

Sea environment complexity and underwater acoustic channels make it hard to extract features of ship-radiated noise signals. This paper presents a novel feature extraction method using the advantages of variational mode decomposition (VMD), fluctuation-based dispersion entropy (FDE) and self-organizing feature map (SOM). Firstly, VMD decomposition of the original signal is used to get a group of bandwidth-limited intrinsic mode functions (IMFs). Then, the difference between the FDE of each IMF and the original signal is calculated, respectively; the IMF with the smallest difference (SIMF) is selected to calculate the FDE as the feature vector. Finally, the characteristic vectors are sent to the SOM classifier to categorize the original signal. The proposed method is applied to feature extraction of real ship-radiated noise signals. The results show that this method is more precise for ship-radiated noise signals feature extraction.

scholarly journals Faulty Line Selection Based on Modified CEEMDAN Optimal Denoising Smooth Model and Duffing Oscillator for Un-Effectively Grounded System

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Sizu Hou ◽  
Wei Guo
Keyword(s):  
Signal Detection ◽  
Empirical Mode Decomposition ◽  
Duffing Oscillator ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Smooth Model ◽  
Zero Sequence ◽  
Line Selection

As the un-effectively grounded system fails, the zero-sequence current contains strong noise and nonstationary features. This paper proposes a novel faulty line selection method based on modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) and Duffing oscillator. Here, based on multiscale permutation entropy, fuzzy c-means clustering, and general regression neural network for abnormal signal detection, the MCEEMDAN is proposed. The endpoint mirror method is used to suppress the endpoint effect problem in the decomposition stage. The proposed algorithm is able to decompose the original signal into a series of intrinsic mode functions, which can complete the first filtering. The research shows that it can efficiently suppress the mode confusing phenomenon of empirical mode decomposition (EMD) and is also more complete and orthogonal than ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD). The optimal denoising smooth model is established for choosing optimal intrinsic mode functions to complete the second filtering. It can ensure that the reconstructed filtered signal has better smoothness and similarity. The optimal denoising smooth model of MCEEMDAN can not only keep useful details of the original signal but also reduce the noise and smooth signal. The bifurcation characteristic of the chaotic oscillator is applied in weak signal detection. The zero-sequence current’s denoising result is extracted as the input signal of the Duffing system. The faulty line could be selected by observing the phase diagram of the system. The research results verify the usability and effectiveness of the proposed method.

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