Papers by Keyword: EEMD

Abstract: Rotor axis orbit measurement of the spindle under cutting process is the important basis of cutting quality judgment and spindle fault diagnosis. In the cutting process, the non-stationary characteristics of vibration measurement signal of spindle rotor has been particularly outstanding, under such circumstances it is difficult to draw spindle rotor axis orbit accurately. To solve this problem, a new method using EEMD (Ensemble Empirical Mode Decomposition) and harmonic wavelet is put forward to realize non-stationary signal purification and rotor axis orbit feature extraction under machine tool spindle cutting process. In order to filter out the high frequency noise of the measurement, the EEMD method has been used, in addition that the characteristics of the original signal is preserved well. However the signal after EEMD filter still contains a variety of frequency components, in order to solve it, the harmonic wavelet method is used to decompose the signal into several signals according to the different frequency components, through the signal reconstruction achieve rotor axis orbit feature extraction. Using this method, the machined workpiece has been cut under the speed of 12000r/min, the vibration of spindle has been measured and processed. The experiment results show that the new method can effectively reduce the high frequency interference noise signal, and also apparently the rotor axis orbit obtained is more clearly than the original rotor axis orbit.

Abstract: When an early fault turns up in rotating machinery, the normal vibration signal will be modulated with periodic transient shock pulses. It’s significant to diagnose these periodic shock pulses for early fault prognostic and diagnostic tests. Usually demodulating is one of the most effective and common used method. Because of the strong background noise, it’s very difficult to select the parameters of band-pass filter. In this paper, we propose to use Ensemble Empirical Mode Decomposition (EEMD) coordinating with spectrum kurtosis theory to choose the Intrinsic Mode Functions (IMFs) to reduce the background noise and select the parameters of band-pass filter adaptively by fast-kurtogram. Energy operator demodulating method is used to demodulate the rebuilt signal to identify the faults frequencies. Energy operator demodulating displays better accuracy and little edge error. The achieved accuracy in the simulation indicates that this proposed transient faults diagnosis method is highly reliable and applicable in early transient faults diagnosis of industrial rotating machinery.

Abstract: Aiming at the de-noising of GPR echo signal, a de-noising method based on EEMD and wavelet is presented. First the echo signal data is processed with EEMD and yields IMF components. Then the IMF components which indicate noise are subtracted. Next, the high frequency IMF components of the remaining are subjected to wavelet threshold. Finally, the signal is reconstructed using the de-noising IMF and low frequency IMF to realize signal de-noising. Compared with other commonly used methods, EEMD-wavelet method has improvement on SNR. The experiment results show its effectiveness and feasibility in GPR de-noising.

Abstract: Dynamical properties of mechanical systems can be obtained with the vibration signals from the systems. However, for the influence of noises, it is difficult to accurately acquire the features. Therefore, de-noising operation is significant for vibration signal in the practical engineering. In order to resolve this problem, the Ensemble Empirical Mode Decomposition (EEMD) method is introduced to try to eliminate noises from the analyzed signal. At first, the theory of the method is illustrated, which included adding white noises, EMD for the signal and calculating the mean of the intrinsic mode function. On this base, the signal which contains several harmonic components with white noise is processed by EEMD. As the result shown, the random noise can be effectively removed; moreover, the harmonic components can be accurately separated. And these improve that the EEMD is an effective method for the de-noising.

Abstract: High voltage circuit breaker is one of the most significant devices and its health status will impact security of the power system. In this paper, the method of high voltage circuit breakers mechanical fault diagnosis is discussed, fault diagnosis method based on vibration signal is proposed. Firstly, the collected acoustic signals are proceed by blind source separation processing through fast independent component analysis. Then, the acoustic signal feature vector is extracted by improved ensemble empirical mode decomposition (EEMD) and the residual signal is filtered by fractional differential. Finally, the feature vectors are input into support vector machine (SVM) for fault diagnosis. Experiment shows that the proposed method can get more precise fault classification to high voltage circuit breakers.

Abstract: A novel method which is based on ensemble empirical mode decomposition (EEMD) and symbolic time series analysis (STSA) was proposed in this paper. Firstly, the vibration signal of hydraulic pump was decomposed into a number of stationary intrinsic mode functions (IMFs). Secondly, the sensitive component was extracted. Finally, the relative entropy (RE) was extracted from the sensitive components and they were used as the indicator to distinguish the faults of hydraulic pump. The research results of actual testing vibration signal demonstrated the rationality and effectiveness of the proposed method in this paper.

Abstract: Aims at the difficult to measure and influence serious in hydraulic impact fault, the intelligent diagnosis of hydraulic impact fault is proposed with improved EEMD and SVM in this paper. Three states of normal and sudden stop and suddenly reversing shock are set up on the hydraulic experimental bench. The improve EEMD is proposed by EEMD noise reduction, SVM extension signal, cubic spline interpolation improvement, and related pseudo components eliminating. The intelligent fault diagnosis in hydraulic system is researched by the improve EEMD to extract the IMF energy as feature vector, and the SVM training classification. Because distinguish and clear between normal state samples with the two impact fault samples, classification results are very right under a linear, polynomial kernel function, or a RBF, sigmoid and precomputed kernel function.

Abstract: In order to diagnose nonlinear and non-stationary fault signals in bearings, a new method is presented based on the ensemble empirical decomposition (EEMD) and the fuzzy c-means (FCM) clustering algorithm. At first, the bearing fault signals were decomposed using EEMD and the intrinsic mode functions (IMF) were produced. Second the energy ratios of these IMFs were computed and taken as the characteristic parameters for the FCM clustering algorithm. Then the FCM clustering method was conducted to classify the bearing fault signals into different classes. Finally, on the basis of the preceding classification results, we diagnosed a bearing fault through taking its distances between different cluster centers as the criteria. Experiments showed that the bearing fault signal classification results conformed to actualities well. The new signal classification method can be effectively utilized in bearing fault diagnosis.

Abstract: To fault diagnosis of diesel engine, put forward a fault diagnosis of diesel engine based on EEMD difference energy spectrum of singular value and RBF. Nonstationary original acceleration vibration signal of kinds of diesel engine’s working condition is separated to several IMF and structure a Hankel matrix by the IMF for singular value decomposition, then de-noise and reconstruction one IMF on the basis of the theory of singular value difference spectrum, and use the reconstructed IMF’s energy which include fault information as the income of RBF. This method can judge the kinds of diesel engine’s working condition and fault types accurately in the experiment.

Abstract: Mechanical failure of high voltage circuit breaker accounted for the largest percentage of, it is necessary to diagnosis the mechanical fault .The acoustic signal of high voltage circuit breaker contains a large number of mechanical state information, can put the acoustic signal characteristics as a basis for high voltage circuit breaker fault diagnosis. M - RVM expanding traditional RVM to multiple categories, very suitable for fault diagnosis of high voltage circuit breaker.In this paper, the M-RVM combined with EEMD method for high voltage circuit breaker mechanical fault diagnosis, the experimental results show that the method has a good diagnosis effect.