Bearings are the most important and most used components in different industries. Early bearing fault diagnosis can prevent human and financial losses. One of the best methods for fault diagnosis of these elements is via vibration analysis. In this paper EMPIRICAL MODE DECOMPOSITION (EMD) which is a fairly new signal processing method of nonlinear and nonstationary signals is used for analyzing vibration signals extracted from bearings. This method was proposed by Huang in 1998. In this research, extracted signal from healthy and faulty bearings are decomposed in to EMPIRICAL MODEs. By analyzing different EMPIRICAL MODEs from 8 derived EMPIRICAL MODEs for healthy and faulty bearings under different load conditions from zero to three horsepower, the first MODE has the most information to classify bearing condition. From the first EMPIRICAL MODE six features in time domain were calculated for healthy bearing, bearing with inner race fault, bearing with outer race fault and bearing with ball fault. These eight features were used as input vector to a designed ANFIS network for bearing condition classification. The ANFIS network was able to detect different condition of bearing with 100% precession.

Potential field anomalies are usually superposed large-scale structures and small-scale structures anomalies. Separation of these two categories of anomalies is the most important step in the data interpretation. Different methods have been introduced for these types of works, but most of them are the semi-automatic methods. In this paper, EMPIRICAL MODE DECOMPOSITION method is used to differentiate regional and residual anomalies. This automatic method is based on extraction of the intrinsic oscillatory MODEs of data. Efficiency of this method is investigated on both synthetic and real data acquired on Tromspberg area of South Africa. Different results show that this technique have higher accuracy than conventional methods like as polynomial fitting.

The quality of seismic data varies tremendously, from areas where excellent reflections (or refractions) are obtained to areas in which the most MODErn equipment, complex field techniques, and sophisticated data processing do not yield usable data. Between these extremes, lie most areas in which useful results can be obtained. Seismic records are generally affected by various types of noise, such as ground rolls, multiples, random noise, and reflection and reflected refraction from near surface structures. Random noise resultiing from random oscillation during data acquisition is one of the most important and harmful noises that exists in seismic data over all times and frequencies. Many efforts have been made to remove this type of noise from seismic data. The predictive filter is an ordinary method commonly used for random noise attenuation from seismic data. This filter can be used in various domains, such as the f-x domain (Haris and White, 1997) and the discrete Cosine domain (Lu and Liu, 2007). Jones and Levy (1987) removed events which were not coherent trace-to-trace events by means of the Karhunen-Loeve transform.The EMPIRICAL MODE DECOMPOSITION (EMD) method is an algorithm for the analysis of multicomponent signals that breaks them down into a number of amplitude and frequency modulated zero-mean signals, termed intrinsic MODE functions (IMFs). An IMF must fulfill two requirements: (1) the number of extrema and the number of zero crossings are either equal or differ at most by one; (2) at any point, the mean value of the envelope defined by the local maxima and the envelope defined by the local minima is zero.In contrast to conventional DECOMPOSITION methods such as wavelets, which perform the analysis by projecting the signal under consideration onto a number of predefined basis vectors, EMD expresses the signal as an expansion of basic functions that are signal-dependent and estimated via an iterative procedure called sifting. Apart from the specific applications of EMD, a more generalized task in which EMD can prove useful is signal denoising (Kopsinis and McLaughlin, 2009). When EMD is used for denoising, the problem is to identify properly which IMFs contain noise characteristics. Certain MODEs will consist mainly of noise, whereas other MODEs will contain both signal and noise characteristics. In the case of white Gaussian noise, the noise-only energy of the MODEs decreases logarithmically. The first MODE, carrying the highest amount of noise energy, will consist mainly of noise, and the effect of noise should gradually weaken with higher MODEs.In this paper, a new signal denoising method based on the EMPIRICAL MODE DECOMPOSITION framework is used to suppress random noises in seismic data. A Noisy signal is decomposed into oscillatory components (IMFs). The EMPIRICAL MODE DECOMPOSITION denoising method involves filtering each intrinsic MODE function and reconstructing s the estimated signal using the processed intrinsic MODE functions. The direct application of wavelet-like thresholding to the DECOMPOSITION MODEs is, in principle, wrong and can have catastrophic consequences regarding the continuity of the reconstructed signal. This arises as a result of the special attributes of IMFs; namely, they resemble an AM/FM modulated sinusoid with zero mean. Consequently, we used the interval thresholding method instead of direct theresholding method to denoise seismic signal.the efficiency of the proposed method was tested on both synthetic and real seismic data. In every case, results show that the denoising algorithm can suppress random noise significantly.

Random Telegraph Noise (RTN) is a stochastic phenomenon which leads to characteristic variations in electronic devices. Finding features of this signal may result in its MODEling and eventually removing the noise in the device. Measuring this signal is accompanied by some noise and therefore we require a method to improve the Signal to Noise Ratio (SNR). As a result, the extraction of an accurate RTN is a remarkable challenge. EMPIRICAL MODE DECOMPOSITION (EMD) as a fully adaptive and signal dependent method, with no dependency to the specific function, can be an appropriate solution. In this paper, we evaluate the most recent methods and compare them with our proposed approach for the artificial and actual RTN signals. The results show the higher accuracy and efficiency by about 54%, 61% and 39% improvement in SNR, Mean Square Error (MSE) and Percent Root mean square Difference (PRD) respectively for the optimized wited method. Finally, an indicator to evaluate the reliability in digital circuits is introduced.

Real time vibrational signal processing is one of the fault detection methods for the mechanical systems. The Hilbert– Huang Transform (HHT) is a newly developed robust method for analyzing nonlinear and non-stationary vibrations based on time-frequency distribution. This approach is based on EMPIRICAL MODE DECOMPOSITION (EMD) and Hilbert spectral analysis. This paper presents a state-of-the-art method for decomposing a signal into a set of so-called Intrinsic MODE Functions (IMF). The proposed alternative method is based on the change in the screening algorithm. This modified method is useful to mitigate end effects and reduces the calculation load and time. The effectiveness of this method was validated by numerical simulation. The results show the accuracy and reliability of this method.

This study describes an approach to identify multiple flicker sources at the point of common coupling (PCC). The voltage signals of different flicker sources such as the electrical arc furnace, the fixed-speed wind turbine, and the diesel-engine driven generator were recorded at the PCC. For this purpose, various aerodynamic and mechanical faults of a wind turbine such as wind shear and tower shadow, gearbox tooth-breaking, blade crash, pitch angle error and various mechanical faults of diesel-engine driven generator such as misfiring, exciter, and governor error, are considered. After acquiring voltage signals of various faults, the EMPIRICAL MODE DECOMPOSITION (EMD) as a robust signal processing technique for extracting useful features was used. Then, for reducing required memory space and computational burden, the minimal-redundancy-maximal-relevance (MRMR) and the symmetric uncertainty (SU) as the feature selection methods were applied. Also, for increasing the efficiency of feature selection methods, the cooperative game-theoretic method was utilized. Afterward, two classifiers based on the Naive-Bayes and the support vector machine (SVM) are used to detect the faults. Simulation results are presented to validate the effectiveness of the proposed method.

Background: The precise evaluation of tissue permeability using the Magnetic Resonance Imaging (MRI) method requires high-quality images. Due to noisy acquired dynamic MRI images, some methods of processing are required to obtain the imaging detail of interest. Objective: This study aimed to implement EMPIRICAL MODE DECOMPOSITION (EMD) to the Lock-Locker (LL) images to improve the permeability of the normal tissue and tumor region, evaluated by the Quantitative Autoradiography (QAR) method. Material and Methods: In this experimental and analytical study, the EMD method was used to improve the tissue permeability from the LL-MRI images of the rat brain. The EMD components were extracted from LL images, and the resulting components were combined using different weighting factors. The tissue permeability was derived by extracting the information of each pixel from the LL image series and fitting curves to the data. Results: The optimum weighted combination factors images were 0. 7 for the middle and low-frequency components and 1 for the high-frequency component. The calculated tissue permeability was between 0. 0023-0. 0043 (ml. min-1. g-1) for abnormal tissue. Conclusion: The estimation of the permeability of tumors in the rat brain with the LL images and the processed LL images by the EMD method shows that the EMD method and the weighted combination of frequency components can improve the permeability calculation in the LL images for the rat brain. The results of permeability estimation by EMD due to noise reduction of LL images are closer to the values obtained from the Quantitative Autoradiography (QAR).