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The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing.
to detect abnormalities. Therefore, analysis of ECG signals using a computer-aided tools, potentially helps physicians to efficiently identify abnormalities [ 4 , 5 ].
The four major stages in a heartbeat abnormalities diagnosis procedure are preprocessing, featureextraction, feature selection, and classification [ 6 ]. Various types of artifacts and noise usually contaminate ECG recordings. In the preprocessing stage, the goals are to decrease such artifacts and noise and to improve the signal for subsequent processing.
As an important step, feature
Deep convolutional neural networks (CNNs) are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.
The current paper presents a method to deliver non- linear projections of a data set that discriminate between existing labeled groups of data items. Inspired from traditional linear Projection Pursuit and Linear Discriminant Analysis, the new method seeks nonlinear combinations of attributes as polynomials that maximize Fisher’s criterion. The search for the monomials in a polynomial is conducted in a logarithmic space in order to reduce computational complexity. The selection of monomials and the optimization of weights that conduct to the nonlinear projection are performed with a multi-modal Genetic Algorithm hybridized with Differential Evolution. By alleviating the drawbacks driven from the linearity assumptions in traditional Projection Pursuit, the new method could gain a wide applicability in both unsupervised and supervised data analysis.
/PMUjour Advances in Elec- trical and Computer Engineering vol12.
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 YANG, Y. Y.-LU, J. M.-ROVNAK, J.-QUACKENBUSH, S. L.-LUNDQUIST, E. A. : SWAN-1, a Caenorhabditis Ele- gans WD Repeat Protein of the AN11 Family, is a Negative Reg- ulator of rac GTPase Function, Genetics 174 (2006), 1917
Nowadays, research in area of intelligent transportation system is focused on analysis of traffic flow by computer vision techniques. Keypoint analysis represents detection, modelling and recognition of objects in traffic flow and object tracking as well. The main goal of this paper is to propose new approach to detect and modelling 3D objects that move on road surface. At the beginning, basic methods of object recognition and modelling of 3D object are shortly described. The modified algorithm based on background subtraction and creation of 3D model by depth map is proposed. Finally, the results of developed algorithm are depicted through the last part of this paper.