Abstract: A device for processing a digital signal includes a Farrow structure (14) that applies to the digital signal a time-varying sample rate conversion from the fixed sample rate to a time varying sampling. The digital signal sampled at the time varying sampling is a resulting signal. The Farrow structure (14) is controlled from a control variable. A spectral analysis means (15) performs a spectral analysis of the resulting signal to determine the frequency values of the resulting signal. A determining means (16) determines a sparseness parameter of the frequency values of the resulting signal. A controlling means (17) modifies the control variable according to the value of the sparseness parameter.

Abstract: A bearing device includes a bearing having an inner ring and an outer ring, at least one of the inner and outer rings being capable of rotating concentrically relative to the other ring. A fiber optic sensor includes an array of optical strain gauges mounted on the inner ring or on the outer ring of the bearing and an optical interrogator is configured to measure the deformations of the inner ring or the outer ring from the optical strain gauges. A command device commands the optical interrogator, a first determining device determines a statistical parameter from random samples of measurements, and a second determining device determines the load acting on the bearing from the statistical parameter and a predetermined relationship between the statistical parameter and the load.

Abstract: A method for digital processing of vibration signals from rolling bearings in rotating machines is presented, allowing subsequent fault detection with high reliability. A linear, adaptive filter is applied to the acquired vibration signal and iteratively tuned to increase the statistical asymmetry of its output. In this process, the filter removes phase- and amplitude distortion from underlying fault impulses. Furthermore, suppression of sinusoidal disturbances is simultaneously achieved with high robustness to measurement noise. The result is a processed signal from which rolling bearing defects are more easily detected.

Abstract: A method for digital processing of vibration signals from rolling bearings in rotating machines is presented, allowing subsequent fault detection with high reliability. A linear, adaptive filter is applied to the acquired vibration signal and iteratively tuned to increase the statistical asymmetry of its output. In this process, the filter removes phase- and amplitude distortion from underlying fault impulses. Furthermore, suppression of sinusoidal disturbances is simultaneously achieved with high robustness to measurement noise. The result is a processed signal from which rolling bearing defects are more easily detected.