Wavelet Decomposition for the Detection and Diagnosis of Faults in Rolling Element Bearings

Condition monitoring and fault diagnosis of equipment and processes are of great concern in industries. Early fault detection in machineries can save millions of dollars in emergency maintenance costs. This paper presents a wavelet-based analysis technique for the diagnosis of faults in rotating machinery from its mechanical vibrations. The choice between the discrete wavelet transform and the discrete wavelet packet transform is discussed, along with the choice of the mother wavelet and some of the common extracted features. It was found that the peak locations in spectrum of the vibration signal could also be efficiently used in the detection of a fault in ball bearings. For the identification of fault location and its size, best results were obtained with the root mean square extracted from the terminal nodes of a wavelet tree of Symlet basis fed to Bayesian classier

Half-quadratic regularization for MRI image restoration

We consider the reconstruction of MRI images by minimizing regularized cost-functions. To accelerate the computation of the estimate, two forms of half-quadratic regularization, multiplicative and additive, are often used. In Nikolova and Ng (2002), we have compared both theoretically and experimentally the efficiency of these two forms using one-dimensional signals. The goal of this paper is to compare experimentally the efficiency of these two forms using MRI image reconstruction. We find that using the additive form is more computationally effective than using the multiplicative form.published_or_final_versio

Robust human face detection in complex color images

We propose in this paper a model based technique for the detection of human faces from rich still color images. Traditionally, color images are represented in the RGB color space. RGB space, however, is not only a 3-dimensional space but also includes brightness or luminance which is not a reliable criterion for skin separation. To avoid the effect of luminance, we propose to work in the chromatic or pure color space. Using such space, a Gaussian model for the skin color pixels is developed and a skin likelihood image is obtained. Such image is then transformed into a binary image using adaptive thresholding. Finally, bright regions satisfying certain "facial" properties are obtained followed by a template matching stage. The method presented here is shown to provide robust detection under different environments and found to achieve very satisfactory results when compared to traditional "mug shot" based approaches

Wideband audio compression using wrapped linear prediction and the discrete wavelet transform

In this paper, an audio coder using the discrete wavelet transform (DWT) and a warped linear prediction (WP) model, is proposed. In contrast to conventional LP, WLP allows for the control of frequency resolution closely match the response of the human auditory system. The residual from the inverse WLP filtering is analyzed by a wavelet filterband designed to approximate the critical bands. For monophonic signals sampled at 44.1 KHz, the coder achieves near transparent quality at an average bit-rate of 64Kb/s

Robust human face detection in complex color images

We propose in this paper a model based technique for the detection of human faces from rich still color images. Traditionally, color images are represented in the RGB color space. RGB space, however, is not only a 3-dimensional space but also includes brightness or luminance which is not a reliable criterion for skin separation. To avoid the effect of luminance, we propose to work in the chromatic or pure color space. Using such space, a Gaussian model for the skin color pixels is developed and a skin likelihood image is obtained. Such image is then transformed into a binary image using adaptive thresholding. Finally, bright regions satisfying certain "facial" properties are obtained followed by a template matching stage. The method presented here is shown to provide robust detection under different environments and found to achieve very satisfactory results when compared to traditional "mug shot" based approaches

Wideband audio compression using wrapped linear prediction and the discrete wavelet transform

In this paper, an audio coder using the discrete wavelet transform (DWT) and a warped linear prediction (WP) model, is proposed. In contrast to conventional LP, WLP allows for the control of frequency resolution closely match the response of the human auditory system. The residual from the inverse WLP filtering is analyzed by a wavelet filterband designed to approximate the critical bands. For monophonic signals sampled at 44.1 KHz, the coder achieves near transparent quality at an average bit-rate of 64Kb/s

Soft constraint satisfaction multimodulus blind equalization algorithms

In this work, a new algorithm, based on the minimum-disturbance principle with relaxation, is presented for the blind equalization of complex signals. This algorithm combines the benefits of the well-known reduced constellation algorithm (RCA) and constant modulus algorithm (CMA). The convergence characteristics of the proposed algorithm are demonstrated by way of simulations. In addition, closed form expressions are obtained for the statistical (dispersion) constants used in these algorithms