Instantaneous Frequency Estimation of Discrete Time Signals

The classical concept of instantaneous frequency, obtained by differentiating the instantaneous phase is one of the most used approaches. Nonlinear signals usually have nonlinear and nonstationary behavior. Revealing hidden properties of time discrete signals could be important in understanding specific phenomena or processes. This paper uses simulated signals to prove the utility of instantaneous frequency estimation in dedicated signal processing. The procedure is based on empirical mode decomposition of the signal into monocomponents. The Hilbert transform of these monocomponents reveals they instantaneous frequencies. There are certain mathematical requirements and limitations for signals that the proposed procedure could perform proper instantaneous frequency estimation. The used signals are artificial and the procedure is carried out in MATLAB

Adaptive time-frequency distribution for accurate representation of radar signals

Electronic Support is one of the key elements in electronic warfare where the main interest is to detect and classify emitted radar signals. Quadratic time-frequency distribution (TFD) is often used to represent this type of signal due to its high resolution representation in time and frequency. However, it is greatly affected by the cross-terms which cause inaccurate signal interpretation. The purpose of this study is to design a cross-term suppression technique for a non-cooperative environment where the exact signal characteristics are unknown. A new adaptive directional ambiguity function Wigner-Ville distribution (ADAF-WVD) is developed to adaptively estimate the kernel parameters based on the ambiguity properties of a signal. Two adaptive procedures, which are the Doppler-lag block searching and the ambiguity domain energy concentration estimation are developed to separate the auto-term from the cross-term in the ambiguity domain. ADAF-WVD measures the energy level of the signal in the ambiguity domain to distinguish between the auto-terms and cross-terms. Four radar signal types are used to verify the accuracy of the time-frequency representation (TFR): simple pulse, Costas coded, pulsed linear frequency modulation and continuous wave linear frequency modulation. Accurate TFRs are produced for most of the signal as low as at signal-to-noise ratio (SNR) of -1 dB. The performance of instantaneous frequency estimation is verified using Monte Carlo simulation. Both approaches are proven to be efficient estimators as they meet the requirements of the Cramer-Rao Lower Bound at SNR > 6 dB. The computational complexity of ADAFWVD is four times lower than the adaptive smooth window cross Wigner-Ville distribution. Thus, it has been demonstrated that the developed TFD is an efficient solution for the analysis of radar signals

Fault Diagnosis in the Slip Frequency Plane of Induction Machines Working in Time-Varying Conditions

[EN] Motor current signature analysis (MCSA) is a fault diagnosis method for induction machines (IMs) that has attracted wide industrial interest in recent years. It is based on the detection of the characteristic fault signatures that arise in the current spectrum of a faulty induction machine. Unfortunately, the MCSA method in its basic formulation can only be applied in steady state functioning. Nevertheless, every day increases the importance of inductions machines in applications such as wind generation, electric vehicles, or automated processes in which the machine works most of time under transient conditions. For these cases, new diagnostic methodologies have been proposed, based on the use of advanced time-frequency transforms-as, for example, the continuous wavelet transform, the Wigner Ville distribution, or the analytic function based on the Hilbert transform-which enables to track the fault components evolution along time. All these transforms have high computational costs and, furthermore, generate as results complex spectrograms, which require to be interpreted for qualified technical staff. This paper introduces a new methodology for the diagnosis of faults of IM working in transient conditions, which, unlike the methods developed up to today, analyzes the current signal in the slip-instantaneous frequency plane (s-IF), instead of the time-frequency (t-f) plane. It is shown that, in the s-IF plane, the fault components follow patterns that that are simple and unique for each type of fault, and thus does not depend on the way in which load and speed vary during the transient functioning; this characteristic makes the diagnostic task easier and more reliable. This work introduces a general scheme for the IMs diagnostic under transient conditions, through the analysis of the stator current in the s-IF plane. Another contribution of this paper is the introduction of the specific s-IF patterns associated with three different types of faults (rotor asymmetry fault, mixed eccentricity fault, and single-point bearing defects) that are theoretically justified and experimentally tested. As the calculation of the IF of the fault component is a key issue of the proposed diagnostic method, this paper also includes a comparative analysis of three different mathematical tools for calculating the IF, which are compared not only theoretically but also experimentally, comparing their performance when are applied to the tested diagnostic signals.This work was supported by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i -Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Burriel-Valencia, J.; Riera-Guasp, M. (2020). Fault Diagnosis in the Slip Frequency Plane of Induction Machines Working in Time-Varying Conditions. Sensors. 20(12):1-26. https://doi.org/10.3390/s20123398S126201