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.

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.

scholarly journals An Improved Empirical Mode Decomposition Method for Vibration Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaohan Liu ◽  
Guangfeng Shi ◽  
Weina Liu
Keyword(s):  
Signal Processing ◽  
Power Spectrum ◽  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Blasting Vibration ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

With the development of electronic measurement and signal processing technology, nonstationary and nonlinear signal characteristics are widely used in the fields of error diagnosis, system recognition, and biomedical instruments. Whether these features can be extracted effectively usually affects the performance of the entire system. Based on the above background, the research purpose of this paper is an improved vibration empirical mode decomposition method. This article introduces a method of blasting vibration signal processing—Differential Empirical Mode Decomposition (DEMD), combined with phosphate rock engineering blasting vibration monitoring test, and Empirical Mode Decomposition (EMD) to compare and analyze the frequency screening of blasting vibration signals, the aliasing distortion, and the power spectrum characteristics of the decomposed signal. The results show that compared with EMD, DEMD effectively suppresses signal aliasing and distortion, and from the characteristics of signal power spectrum changes, DEMD extracts different dominant frequency components, and the frequency screening effect of blasting vibration signals is superior to EMD. It can bring about an obvious improvement in accuracy, and the calculation time is about 4 times that of the EMD method. Based on the ground analysis of ground motion signals, this paper uses the EMD algorithm to analyze measured ground blast motion signals and study its velocity characteristics and differential time, which provides a new way of studying motion signals.

Enhanced fault diagnosis of roller bearing elements using a combination of empirical mode decomposition and minimum entropy deconvolution

2016 ◽  
Vol 231 (4) ◽  
pp. 655-671 ◽  
Author(s):  
Z Zhang ◽  
M Entezami ◽  
E Stewart ◽  
C Roberts
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Roller Bearing ◽  
Fault Detection And Diagnosis ◽  
New Technique ◽  
Minimum Entropy ◽  
Processing Chain ◽  
Mode Decomposition ◽  
Detection And Diagnosis

This paper introduces a new signal processing algorithm for vibration-based fault detection and diagnosis of roller bearings. The methodology proposed in this paper is based on the combination of two data-adaptive techniques which are further enhanced through the use of an automatic feature identification mechanism. The new technique, introduced as empirical mode envelope with minimum entropy, combines elements from the empirical mode decomposition (EMD) and minimum entropy deconvolution (MED) approaches with an energy moment technique to improve the feature selection stage of the EMD algorithm. This improvement allows the processing chain to identify early stage roller bearing faults in noisier signals. The energy moment technique is used to automatically identify the most appropriate intrinsic mode function from the EMD process prior to the MED algorithm being applied. This is in contrast to conventional approaches which tend to use the first mode or make selections based on traditional energy techniques. The combination of the adaptive techniques of EMD and MED allows the development of an improved technique for fault detection and diagnosis of signals. Combining these techniques with the energy moment approach allows further improved fault detection in complex non-stationary conditions. The processing chain has been tested using data obtained during laboratory testing. From the experimental results, it is shown that the new technique is capable of the detection of early stage (minor) roller and outer race defects found in tapered-roller-bearings rotating at a variety of speeds and noise scenarios.

Vibration Signal Processing for Gear Fault Diagnosis Based on Empirical Mode Decomposition and Nonlinear Blind Source Separation

2011 ◽  
Vol 42 (10) ◽  
pp. 55-61 ◽  
Author(s):  
Yu Jiang ◽  
Li Qin ◽  
Yuelei Zhang ◽  
Jingping Wu
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Blind Source Separation ◽  
Single Channel ◽  
Source Separation ◽  
Vibration Signal ◽  
Normal Operation ◽  
Mode Decomposition ◽  
Gear Fault

Gear failures happen frequently in the gear mechanisms, and an unexpected serious gear fault may cause severe damage on the machinery. Hence, precise gear fault detection at the early stage is imperative to ensure the normal operation of the machinery. Independent component analysis (ICA) has been paid more and more attention for its powerful ability of separating the useful vibration source from the multi-sensor observations to enhance the fault feature extraction. This is the so called blind source separation (BSS) procedure. However, the popular ICA model may suffer from two limitations. One is the linear mixture assumption, and the other is the lack of sensor channels. Up to now, only limited research considered the nonlinear ICA model in the field of mechanic fault diagnosis, and techniques for the situation where the number of sensor channels is less than the number of independent sources for gear defect detection are scarce. In order to extract the useful source involved with the gear fault characteristics in single-channel vibration signal processing, this work presents a new method based on the empirical mode decomposition (EMD) and nonlinear ICA. The EMD was firstly employed to decompose the vibration signal into a number of intrinsic mode functions (IMFs), and then these IMFs were taken as the multi-channel observations. The post-nonlinear (PNL) ICA model based on the radial basis function (RBF) neural network was applied to the nonlinear BSS procedure on the IMFs. The experimental vibration data acquired from the gear fault test-bed were processed for the validation of the proposed method. The nonlinear ICA method has been compared with the linear ICA and non-ICA based approaches. The analysis results show that the sensitive characteristics of the gear meshing vibration can be separated from the single channel measurement by the proposed method, and the fault diagnosis precision can be enhanced significantly. The detection rate can be increased by 3.75% or better when the ICA based preprocessing is carried out, and the proposed nonlinear ICA outperforms the linear ICA detection model.

scholarly journals Utilisation of Ensemble Empirical Mode Decomposition in Conjunction with Cyclostationary Technique for Wind Turbine Gearbox Fault Detection

2020 ◽  
Vol 10 (9) ◽  
pp. 3334 ◽  
Author(s):  
Sanaz Roshanmanesh ◽  
Farzad Hayati ◽  
Mayorkinos Papaelias
Keyword(s):  
Signal Processing ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Noisy Environment ◽  
Wind Turbine Gearbox ◽  
Multiple Faults ◽  
Mode Decomposition ◽  
Processing Techniques ◽  
Signal Processing Techniques

In this paper the application of cyclostationary signal processing in conjunction with Ensemble Empirical Mode Decomposition (EEMD) technique, on the fault diagnostics of wind turbine gearboxes is investigated and has been highlighted. It is shown that the EEMD technique together with cyclostationary analysis can be used to detect the damage in complex and non-linear systems such as wind turbine gearbox, where the vibration signals are modulated with carrier frequencies and are superimposed. In these situations when multiple faults alongside noisy environment are present together, the faults are not easily detectable by conventional signal processing techniques such as FFT and RMS.

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.

Study on Vibration Signal Marginal Spectrum of Paper-Transferring System in Printing Press Based on EMD

2010 ◽  
Vol 139-141 ◽  
pp. 2464-2468
Author(s):  
Yi Ming Wang ◽  
Shao Hua Zhang ◽  
Zhi Hong Zhang ◽  
Jing Li
Keyword(s):  
Empirical Mode Decomposition ◽  
Optimization Design ◽  
Impact Load ◽  
Vibration Signal ◽  
Key Factors ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Periodic Signals ◽  
The Moment

The precision of transferring paper is key factors to decide the print overprint accuracy, and vibration has an important impact on paper transferring accuracy. Empirical mode decomposition (EMD) can be used to extract the features of vibration test signal. According to the intrinsic mode function (IMF) by extracted, it is useful to analyze the dynamic characteristics of swing gripper arm on motion state. Due to the actual conditions of printing, the vibration signal of Paper-Transferring mechanism system is complex quasi periodic signals. Hilbert-Huang marginal spectrum that is based on empirical mode decomposition can solve the problem which is modals leakage by FFT calculated in frequency domain. Through the experimental research, the phase information of impact load at the moment of grippers opening or closing, which can be used for the optimization design of Paper-Transferring system and the improvement in the accuracy of swing gripper arm.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

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.

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