The objective of this paper is to outline a methodology for the computation of the response of a multiconductor transmission line terminated by linear networks. The lines are embedded in a multilayered lossy dielectric media and have arbitrary cross sections, but uniform along the length. To check the accuracy of the theoretical results, extensive experimental verification has been carried out.

Analysis of Multiconductor Transmission Lines

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Written for researchers and engineers working in the field, Radar Systems Analysis and Design Using MATLAB provides fundamental radar principles, advanced topics, and rigorous but easy-to-follow mathematical derivations.

Radar Systems Analysis and Design Using MATLAB

This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

/pdf/models-for-bearing-damage-detection-in-induction-motors-zq7k8exor2.pdf

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

This work studies the frequency behavior of a least-square method to estimate the power spectral density of unevenly sampled signals. When the uneven sampling can be modeled as uniform sampling plus a stationary random deviation, this spectrum results in a periodic repetition of the original continuous time spectrum at the mean Nyquist frequency, with a low-pass effect affecting upper frequency bands that depends on the sampling dispersion. If the dispersion is small compared with the mean sampling period, the estimation at the base band is unbiased with practically no dispersion. When uneven sampling is modeled by a deterministic sinusoidal variation respect to the uniform sampling the obtained results are in agreement with those obtained for small random deviation. This approximation is usually well satisfied in signals like heart rate (HR) series. The theoretically predicted performance has been tested and corroborated with simulated and real HR signals. The Lomb method has been compared with the classical power spectral density (PSD) estimators that include resampling to get uniform sampling. The authors have found that the Lomb method avoids the major problem of classical methods: the low-pass effect of the resampling. Also only frequencies up to the mean Nyquist frequency should be considered (lower than 0.5 Hz if the HR is lower than 60 bpm). It is concluded that for PSD estimation of unevenly sampled signals the Lomb method is more suitable than fast Fourier transform or autoregressive estimate with linear or cubic interpolation. In extreme situations (low-HR or high-frequency components) the Lomb estimate still introduces high-frequency contamination that suggest further studies of superior performance interpolators. In the case of HR signals the authors have also marked the convenience of selecting a stationary heart rate period to carry out a heart rate variability analysis.

/pdf/power-spectral-density-of-unevenly-sampled-data-by-least-3lniv8x1ix.pdf

Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals

The heart rate variability (HRV) is an extended tool to analyze the mechanisms controlling the cardiovascular system. In this paper, the integral pulse frequency modulation model (IPFM) is assumed. It generates the beat occurrence times from a modulating signal. This signal is thought to represent the autonomic nervous system action, mostly studied in its frequency components. Different spectral estimation methods try to infer the modulating signal characteristics from the available beat timing on the electrocardiogram signal. These methods estimate the spectrum through the heart period (HP) or the heart rate (HR) signal. The authors introduce a new time domain HRV signal, the Heart Timing (HT) signal. They demonstrate that this HT signal, in contrast with the HR or HP, makes it possible to recover an unbiased estimation of the modulating signal spectra. In this estimation the authors avoid the spurious components and the low-pass filtering effect generated when analyzing HR or HP.

/pdf/improved-heart-rate-variability-signal-analysis-from-the-577pjrmbjk.pdf

R.M. Gray

Papers

Communication systems: An introduction to signals and noise in electrical communication