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Direction of arrival (DOA) estimation is a basic task in array signal processing. A method based on principal component analysis (PCA) is presented for estimating DOA of multiple sources mixed convolutively. Convolutive mixtures of multiple sources in the spatio-temporal domain are firstly reduced to instantaneous mixtures by using the well-known short-time Fourier transformation (STFT) technique...
Independent Component Analysis (ICA) is a powerful tool for redundancy reduction and nongaussian data analysis. And, Artificial Neural Network (ANN), especially the Self-Organizing Map (SOM) based on unsupervised learning is a kind of excellent method for pattern clustering and recognition. By combining ICA with ANN, we proposed a novel compound neural network for fault diagnosis. First, two neural...
A method is proposed for fault diagnosis of rotor systems, with independent component analysis (ICA) based feature extraction and multi-layer perceptron (MLP) based pattern classification. By the use of ICA, feature vectors are integratedly extracted from multichannel vibration measurements collected under different operating patterns (in term of rotating speed and/or load). Thus, a robust multi-MLP...
A class of methods is presented for wholly estimating direction of arrival (DOA) of convolutively mixed sources in the frequency domain, which is based on independent component analysis (ICA). Convolutive mixtures of multiple sources in the spatio-temporal domain are firstly reduced to instantaneous mixtures by using the well-known short-time Fourier transformation (STFT) technique. From the time-frequency...
A novel classifier is proposed for fault diagnosis of rotor system, with independent component analysis (ICA) based feature extraction and multi-layer perceptron (MLP) based pattern classification. By the use of ICA, feature vectors are integratedly extracted from multi-channel vibration measurements collected under different operating patterns (in term of rotating speed and/or load). Thus, a robust...
During the Hilbert-Huang transformation (HHT), time-series data are firstly decomposed into several components with different time scale (i.e. intrinsic mode function, IMF), using the empirical mode decomposition (EMD). Then, the Hilbert transformation is applied to every IMF. As a result, the HHT spectrum of the data is constructed. In this paper, the HHT-based time-frequency representation was used...
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