Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis

Air-borne acoustic based condition monitoring is a promising technique because of its intrusive nature and the rich information contained within the acoustic signals including all sources. However, the back ground noise contamination, interferences and the number of Internal Combustion Engine ICE vibro-acoustic sources preclude the extraction of condition information using this technique. Therefore, lower energy events; such as fuel injection, are buried within higher energy events and/or corrupted by background noise. This work firstly investigates diesel engine air-borne acoustic signals characteristics and the benefits of joint time-frequency domain analysis. Secondly, the air-borne acoustic signals in the vicinity of injector head were recorded using three microphones around the fuel injector (120° apart from each other) and an Independent Component Analysis (ICA) based scheme was developed to decompose these acoustic signals. The fuel injection process characteristics were thus revealed in the time-frequency domain using Wigner-Ville distribution (WVD) technique. Consequently the energy levels around the injection process period between 11 and 5 degrees before the top dead center and of frequency band 9 to 15 kHz are calculated. The developed technique was validated by simulated signals and empirical measurements at different injection pressure levels from 250 to 210 bars in steps of 10 bars. The recovered energy levels in the tested conditions were found to be affected by the injector pressure settings

Exploitation of source nonstationarity in underdetermined blind source separation with advanced clustering techniques

The problem of blind source separation (BSS) is investigated. Following the assumption that the time-frequency (TF) distributions of the input sources do not overlap, quadratic TF representation is used to exploit the sparsity of the statistically nonstationary sources. However, separation performance is shown to be limited by the selection of a certain threshold in classifying the eigenvectors of the TF matrices drawn from the observation mixtures. Two methods are, therefore, proposed based on recently introduced advanced clustering techniques, namely Gap statistics and self-splitting competitive learning (SSCL), to mitigate the problem of eigenvector classification. The novel integration of these two approaches successfully overcomes the problem of artificial sources induced by insufficient knowledge of the threshold and enables automatic determination of the number of active sources over the observation. The separation performance is thereby greatly improved. Practical consequences of violating the TF orthogonality assumption in the current approach are also studied, which motivates the proposal of a new solution robust to violation of orthogonality. In this new method, the TF plane is partitioned into appropriate blocks and source separation is thereby carried out in a block-by-block manner. Numerical experiments with linear chirp signals and Gaussian minimum shift keying (GMSK) signals are included which support the improved performance of the proposed approaches

EOG Artifacts Removal in EEG Measurements for Affective Interaction

A brain-computer interface (BCI) is a direct link between the brain and a computer. Multi-modal input with BCI forms a promising solution for creating rich gaming experience. Electroencephalography (EEG) measurement is the sole necessary component for a BCI system. EEG signals have the characteristics of large amount, multiple channels and sensitive to noise. The amount of valuable information derived from EEG signals is dependent on both the amount of noises embedded in the original measurement and the algorithms selected for postprocessing. Therefore, artifacts removal in the preprocess step is crucial. Electrooculography (EOG) signals are one of the major artifacts that often appear in EEG measurement. In this paper, we compared two different algorithms (Recursive Least Square (RLS) and Blind Source Separation (BSS)) to investigate their performances on removing EOG artifacts from EEG signals. Results indicate that the performance of RLS algorithm is better than BSS algorithm no matter whether there are any EOG reference signals. For BSS algorithm, the performance is better when EOG reference signals are available. These results show that for a BCI system, EEG reference is often necessary. Performance will be sacrificed if an EEG system cannot have any EOG reference signals

A unified approach to sparse signal processing

A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, compo-nent analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding i

Denoising using local projective subspace methods

In this paper we present denoising algorithms for enhancing noisy signals based on Local ICA (LICA), Delayed AMUSE (dAMUSE) and Kernel PCA (KPCA). The algorithm LICA relies on applying ICA locally to clusters of signals embedded in a high-dimensional feature space of delayed coordinates. The components resembling the signals can be detected by various criteria like estimators of kurtosis or the variance of autocorrelations depending on the statistical nature of the signal. The algorithm proposed can be applied favorably to the problem of denoising multi-dimensional data. Another projective subspace denoising method using delayed coordinates has been proposed recently with the algorithm dAMUSE. It combines the solution of blind source separation problems with denoising efforts in an elegant way and proofs to be very efficient and fast. Finally, KPCA represents a non-linear projective subspace method that is well suited for denoising also. Besides illustrative applications to toy examples and images, we provide an application of all algorithms considered to the analysis of protein NMR spectra.info:eu-repo/semantics/publishedVersio