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For good classiication preprocessing is a key step. Good preprocessing reduces the noise in the data and retains most information needed for classiication. Poor preprocessing on the other hand makes classiication almost impossible. In this paper we try to nd good preprocessing for a special type of outlier detection problem, machine diagnostics. We will(More)
OBJECTIVE Many researchers have studied automatic EEG classification and recently a lot of work has been done on artefact-removal from EEG data using independent component analyses (ICA). However, demonstrating that a ICA-processed multichannel EEG measurement becomes more interpretable compared to the raw data (as is usually done in work on ICA-processing(More)
We propose a novel approach to fault detection in rotating mechanical machines: fusion of multichannel measurements of machine vibration using Independent Component Analysis (ICA), followed by a description of the admissible domain (part of the feature space indicative of normal machine operation) with a Support Vector Domain Description (SVDD) method. The(More)
We propose mixtures of hidden Markov models for modelling clickstreams of web surfers. Hence, the page categorization is learned from the data without the need for a (possibly cumbersome) manual categorization. We provide an EM algorithm for training a mixture of HMMs and show that additional static user data can be incorporated easily to possibly enhance(More)
We present a novel approach to signal separation using temporal correlations, which may be useful in decomposition of multichannel vibration data into fault-related harmonic activity. Performance of the algorithm with respect to parameter settings is investigated with artiicial data, whereas vibration data from a submsersible pump is used to compare the(More)
We propose the use of blind source separation (BSS) for separation of a machine signature from distorted measurements. Based on an analysis of the mixing processes relevant for machine source separation, we indicate that instantaneous mixing may hold in acoustic monitoring. We then present a bilinear forms-based approach to instantaneous source separation.(More)
—We propose a noise estimation algorithm for single-channel noise suppression in dynamic noisy environments. A sto-chastic-gain hidden Markov model (SG-HMM) is used to model the statistics of nonstationary noise with time-varying energy. The noise model is adaptive and the model parameters are estimated online from noisy observations using a recursive(More)