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A new approach to estimate the direction-of-arrival (DOA) in a Time-Modulated Array (TMA) system with a bipolar squared periodic time-modulating sequence is proposed in this letter. Using such a sequence can increase the power of harmonic components of the received signal. Based on this fact, we formulated a new parameter pertaining to the ratio of the upper and lower sideband first harmonic components of the received signal, to improve the accuracy of the DOA estimation in a low signal-to-noise ratio (SNR) environment. A numerical analysis was performed to verify the effectiveness of the proposed DOA estimation method.

This study proposes a new approach to estimate the partial discharge (PD) location for power cables. For the PD location estimation, we estimate the time-of-arrival (TOA) interval between the direct and reflected PD pulses using time-domain reflectometry. In the proposed scheme, we introduce a high-resolution technique known as the multiple signal classification method. This method is a powerful tool that extracts the PD pulses in a noisy environment using the orthogonality of the signal vectors to noise eigenvectors. Simulation results of the theoretical and experimental PD signals show that the proposed scheme accurately estimates the TOA interval of the PD pulses in a noisy environment.

This paper proposes a new diagnosis algorithm to detect broken rotor bars (BRBs) faults in induction motors. The proposed algorithm is composed of a frequency signal dimension order (FSDO) estimator and a fault decision module. The FSDO estimator finds a number of fault-related frequencies in the stator current signature. In the fault decision module, the fault diagnostic index from the FSDO estimator is used depending on the load conditions of the induction motors. Experimental results obtained in a 75 kW three-phase squirrel-cage induction motor show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to a zoom multiple signal classification (ZMUSIC) and a zoom estimation of signal parameters via rotational invariance techniques (ZESPRIT).

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

This paper introduces an evaluation code, which can numerically express the noise possessing degree of signals. By using this code, the best kind and setting of noise suppressing techniques can be chosen automatically. This code is applied to three kinds of specific denoising techniques; those are simple noise removing method in the count versus phase distribution, fuzzy logic method based on noise type in magnitude versus phase plot, and lastly, the technique using grouping characteristics of PD pulses in 3D plot of magnitude versus phase versus cycle. The algorithm shows good performance in the various real PD signals measured from various high voltage apparatuses in Korea.

This paper introduces an evaluation code, which can numerically express the noise possessing degree of signals. By using this code, the best kind and setting of noise suppressing techniques can be chosen automatically. This code is applied to three kinds of specific denoising techniques; those are simple noise removing method in the count versus phase distribution, fuzzy logic method based on noise type in magnitude versus phase plot, and lastly, the technique using grouping characteristics of PD pulses in 3D plot of magnitude versus phase versus cycle. The algorithm shows good performance in the various real PD signals measured from various high voltage apparatuses in Korea.