scholarly journals Gearbox Composite Fault Diagnosis Method Based on Minimum Entropy Deconvolution and Improved Dual-Tree Complex Wavelet Transform

Entropy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 18 ◽  
Author(s):  
Ziying Zhang ◽  
Xi Zhang ◽  
Panpan Zhang ◽  
Fengbiao Wu ◽  
Xuehui Li
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Notch Filter ◽  
Vibration Signal ◽  
Coefficient Matrix ◽  
Ensemble Empirical Mode Decomposition ◽  
Minimum Entropy ◽  
Complex Wavelet Transform ◽  
Mode Decomposition ◽  
Complex Wavelet

Dual-tree complex wavelet transform has been successfully applied to the composite diagnosis of a gearbox and has achieved good results. However, it has some fatal weaknesses, so this paper proposes an improved dual-tree complex wavelet transform (IDTCWT), and combines minimum entropy deconvolution (MED) to diagnose the composite fault of a gearbox. Firstly, the number of decomposition levels and the effective sub-bands of the DTCWT are adaptively determined according to the correlation coefficient matrix. Secondly, frequency mixing is removed by notch filter. Thirdly, each of the obtained sub-bands further reduces the noise by minimum entropy deconvolution. Then, the proposed method and the existing adaptive noise reduction methods, such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD), are used to decompose the two sets of simulation signals in comparison, and the feasibility of the proposed method has been verified. Finally, the proposed method is applied to the compound fault vibration signal of a gearbox. The results show the proposed method successfully extracts the outer ring fault at a frequency of 160 Hz, the gearbox fault with a characteristic frequency of 360 Hz and its double frequency of 720 Hz, and that there is no mode mixing. The method proposed in this paper provides a new idea for the feature extraction of a gearbox compound fault.

Two-stage feature selection for bearing fault diagnosis based on dual-tree complex wavelet transform and empirical mode decomposition

2015 ◽  
Vol 230 (2) ◽  
pp. 291-302 ◽  
Author(s):  
Mien Van ◽  
Hee-Jun Kang
Keyword(s):  
Feature Selection ◽  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Complex Wavelet Transform ◽  
Two Stage ◽  
Mode Decomposition ◽  
Complex Wavelet ◽  
Neighbor Classifier

This paper presents an automatic fault diagnosis of different rolling element bearing faults using a dual-tree complex wavelet transform, empirical mode decomposition, and a novel two-stage feature selection technique. In this method, dual-tree complex wavelet transform and empirical mode decomposition were used to preprocess the original vibration signal to obtain more accurate fault characteristic information. Then, features in the time domain were extracted from each of the original signals, the coefficients of the dual-tree complex wavelet transform, and some useful intrinsic mode functions to generate a rich combined feature set. Next, a two-stage feature selection algorithm was proposed to generate the smallest set of features that leads to the superior classification accuracy. In the first stage of the two-stage feature selection, we found the candidate feature set using the distance evaluation technique and a k-nearest neighbor classifier. In the second stage, a genetic algorithm-based k-nearest neighbor classifier was designed to obtain the superior combination of features from the candidate feature set with respect to the classification accuracy and number of feature inputs. Finally, the selected features were used as the input to a k-nearest neighbor classifier to evaluate the system diagnosis performance. The experimental results obtained from real bearing vibration signals demonstrated that the method combining dual-tree complex wavelet transform, empirical mode decomposition, and the two-stage feature selection technique is effective in both feature extraction and feature selection, which also increase classification accuracy.

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 Refactoring and Optimization of Bridge Dynamic Displacement Based on Ensemble Empirical Mode Decomposition

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3125
Author(s):  
Zou ◽  
Chen ◽  
Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Field Experiments ◽  
Low Frequency ◽  
Vibration Signal ◽  
Micro Electro Mechanical System ◽  
Ensemble Empirical Mode Decomposition ◽  
Reconstruction Method ◽  
Monitoring Methods ◽  
Dynamic Displacement ◽  
Mode Decomposition

Considering the lack of precision in transforming measured micro-electro-mechanical system (MEMS) accelerometer output signals into elevation signals, this paper proposes a bridge dynamic displacement reconstruction method based on the combination of ensemble empirical mode decomposition (EEMD) and time domain integration, according to the vibration signal traits of a bridge. Through simulating bridge analog signals and verifying a vibration test bench, four bridge dynamic displacement monitoring methods were analyzed and compared. The proposed method can effectively eliminate the influence of low-frequency integral drift and high-frequency ambient noise on the integration process. Furthermore, this algorithm has better adaptability and robustness. The effectiveness of the method was verified by field experiments on highway elevated bridges.

An Enhanced Extraction Method Based on EEMD for Processing a Bearing Vibration Signal With Multiple Vibration Sources

2014 ◽  
Author(s):  
Wei Guo
Keyword(s):  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Similarity Criterion ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Combination Technique ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Eemd Method ◽  
Effective Signal

Condition monitoring and fault diagnosis for rolling element bearings is an imperative part for preventive maintenance procedures and reliability improvement of rotating machines. When a localized fault occurs at the early stage of real bearing failures, the impulses generated by the defect are relatively weak and usually overwhelmed by large noise and other higher-level macro-structural vibrations generated by adjacent machine components and machines. To indicate the bearing faulty state as early as possible, it is necessary to develop an effective signal processing method for extracting the weak bearing signal from a vibration signal containing multiple vibration sources. The ensemble empirical mode decomposition (EEMD) method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different bands of simple signal components. However, the energy dispersion and many redundant components make the decomposition result obtained by the EEMD losing the physical significance. In this paper, to enhance the decomposition performance of the EEMD method, the similarity criterion and the corresponding combination technique are proposed to determine the similar signal components and then generate the real mono-component signals. To validate the effectiveness of the proposed method, it is applied to analyze raw vibration signals collected from two faulty bearings, each of which involves more than one vibration sources. The results demonstrate that the proposed method can accurately extract the bearing feature signal; meanwhile, it makes the physical meaning of each IMF clear.

scholarly journals Multiple Fault Detection of Rolling Bearing through Ensemble Empirical Mode Decomposition of Vibration Signal

2019 ◽  
Vol 9 (2) ◽  
pp. 2724-2726
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Multiple Faults ◽  
Mode Decomposition

Generally, two or more faults occur simultaneously in the bearings. These Compound Faults (CF) in bearing, are most difficult type of faults to detect, by any data-driven method including machine learning. Hence, it is a primary requirement to decompose the fault vibration signals logically, so that frequencies can be grouped in parts. Empirical Mode Decomposition (EMD) is one of the simplest techniques of decomposition of signals. In this paper we have used Ensemble Empirical Mode Decomposition (EEMD) technique for compound fault detection/identification. Ensembled Empirical Mode Decomposition is found useful, where a white noise helps to detect the bearing frequencies. The graphs show clearly the capability of EEMD to detect the multiple faults in rolling bearings.

scholarly journals Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Liming Fan ◽  
Chong Kang ◽  
Huigang Wang ◽  
Hao Hu ◽  
Mingliang Zou
Keyword(s):  
Anomaly Detection ◽  
Magnetic Anomaly ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Entropy Method ◽  
Magnetic Data ◽  
Magnetic Noise ◽  
Minimum Entropy ◽  
Magnetic Signal ◽  
Mode Decomposition

Due to the fast attenuation of the magnetic field along with the distance, the magnetic anomaly generated by the remote magnetic target is usually buried in the magnetic noise. In order to improve the performance of magnetic anomaly detection (MAD) with low SNR, we propose an adaptive method of MAD with ensemble empirical mode decomposition (EEMD) and minimum entropy (ME) feature. The magnetic data is decomposed into the multiple intrinsic modal functions (IMFs) with different scales by EEMD. According to a defined criterion, the magnetic noise and magnetic signal are reconstructed based on IMFs, respectively. Entropy feature of reconstructed magnetic signal is extracted based on the probability density function (PDF) of the noise which is updated by the reconstructed magnetic noise. Compared to the traditional minimum entropy method, the entropy feature extracted by the proposed method is more obvious. The magnetic anomaly is detected whenever the entropy feature drops below the threshold. Thus, it is effective for revealing the weak magnetic anomaly by the proposed method. The measured magnetic noise is used to validate the performance of the proposed method. The results show that the detection probability of the proposed method is higher with low input SNR.

The Applied Research of the Hilbert-Huang Transform and Wavelet Transform in the Fault Location of Transmission Line

2013 ◽  
Vol 291-294 ◽  
pp. 2432-2436
Author(s):  
Zhi Bin Li ◽  
Bao Xing Wu ◽  
Yun Hui Xu
Keyword(s):  
Wavelet Transform ◽  
Transmission Line ◽  
Empirical Mode Decomposition ◽  
Fault Location ◽  
Ensemble Empirical Mode Decomposition ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Decomposition Processes ◽  
Before And After ◽  
Better Than

In the process of the Hilbert-Huang transform, empirical mode decomposition (EMD) may result in the end effect and modal aliasing when processing data, so proposing Ensemble Empirical Mode Decomposition (EEMD) instead of EMD, and assessing the accuracy of the two decomposition processes according to the total energy of the signal before and after the decomposition. Take a comparison between the Hilbert-Huang transform and the wavelet transform, the localization showed that the Hilbert-Huang transform is better than wavelet transform in the fault location of transmission line.

The Combination of Empirical Mode Decomposition and Minimum Entropy Deconvolution for Roller Bearing Diagnostics in Non-Stationary Operation

2012 ◽  
Author(s):  
R. Ricci ◽  
P. Borghesani ◽  
S. Chatterton ◽  
P. Pennacchi
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Minimum Entropy ◽  
Bearing Diagnostics ◽  
Mode Decomposition ◽  
Data Adaptive ◽  
The Hilbert Transform ◽  
Processing Techniques

Diagnostics is based on the characterization of mechanical system condition and allows early detection of a possible fault. Signal processing is an approach widely used in diagnostics, since it allows directly characterizing the state of the system. Several types of advanced signal processing techniques have been proposed in the last decades and added to more conventional ones. Seldom, these techniques are able to consider non-stationary operations. Diagnostics of roller bearings is not an exception of this framework. In this paper, a new vibration signal processing tool, able to perform roller bearing diagnostics in whatever working condition and noise level, is developed on the basis of two data-adaptive techniques as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED), coupled by means of the mathematics related to the Hilbert transform. The effectiveness of the new signal processing tool is proven by means of experimental data measured in a test-rig that employs high power industrial size components.

Trend Extraction in Vibration Signal Based on EMD

2012 ◽  
Vol 459 ◽  
pp. 377-380
Author(s):  
Yu Hua Dong ◽  
Jun Xing Zhang
Keyword(s):  
Correlation Coefficient ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Coefficient Matrix ◽  
Trend Extraction ◽  
Mode Decomposition ◽  
Effective Combination ◽  
Correlation Coefficient Matrix

This paper proposed a de-trend method for vibration signal of telemetry based on the empirical mode decomposition (EMD) by correlation coefficient matrix. The signal is decomposed to a series of intrinsic mode component and the remainder item by EMD. It mainly distinguishes between the remainder item and the signal trend, according to the correlation coefficient matrix to determine whether some intrinsic mode component belongs to the trend item or not. The results show that signal trends can be extracted accurately through the effective combination EMD with correlation coefficient matrix and the proposed method has good applicability for different signals and different trends.

Sign in / Sign up

Export Citation Format

Share Document