Multi-resolution wavelet analysis for noise reduction in impulse puncture voltage measurements

Santos, Ighor Souza dos; Barbosa, Carlos Roberto Hall; Silva, Márcio Thelio F. da; Filho, Orsino Borges de Oliveira

doi:10.1016/j.measurement.2019.107416

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Fourier and WTs are popular for the analysis of PQ signals for industrial applications and several research articles are already available for different applications. The WT has been used for the detection of transients [43], harmonic distortions [12], voltage sag [4,44], faults in the electrical grid [27,29], and denoising of PQ signals [10]. These PQ anomalies and the most responsible harmonics cause noise, vibrations, harmonic fluxes, heating, and failure of motors.…”

Section: Comparison Of This Study With Existing Studiesmentioning

confidence: 99%

“…One of the most common methods used for FDD is signal processing techniques, such as Fourier transform (FT), S-transform (ST), and wavelet transform (WT) [6]. The Fourier analysis of current and voltage waveforms using fast Fourier transform (FFT), along with Kalman filter and ST, are the most commonly used methods for analysis in the frequency domain [7][8][9][10][11]. However, Fourier analysis or ST alone is not enough for the detection of low-magnitude transients because of their sensitivity over discontinuities.…”

Section: Introductionmentioning

confidence: 99%

“…Multiresolution WT is used for the localization and detection of a fault in DC microgrids [29]. The multiresolution WT is also used for noise reduction in voltage puncture testing [10]. In [30], a comparison of the WT and FFT is conducted for PQ parameters in balanced and unbalanced conditions.…”

Section: Introductionmentioning

confidence: 99%

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Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

et al. 2021

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In modern power systems, since most loads are inductive by nature, there is an ongoing power quality issue and researchers’ interest in improving the power factor is widespread, as inductive loads have a low power factor that depletes the system’s capacity and has an adverse effect on the voltage level. The measurement and acute analysis of voltage- and current-level waveforms is essential to tackle power quality issues. This article presents a detailed case study and analysis of real-time data measured from a frequency converter, which is used to operate the motor of a ventilation system. The output of the frequency converter is a highly distorted current wave. A hybrid Fourier transform (FT)- and wavelet transform-based solution has been proposed here to diagnose and identify the causes of motor failure in the ventilation system. The traditional FT did not give a detailed analysis of this type of signal, which is highly contaminated by noise. Therefore, first, the signal is preprocessed for data denoising using the wavelet transform. Second, the Fourier analysis is performed on the filtered signal for frequency analysis and segregation of fundamental frequency components, higher-order harmonics, and suppressed noise. The spectrum analysis reveals that the noise is generated due to the rapidly switching circuits in the frequency converter and this unfiltered signal at the output of the frequency converter causes motor failure.

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Section: Comparison Of This Study With Existing Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

et al. 2021

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“…Data obtained in practical engineering contains uncertainty because of restrictions on the working environment or test cost [2]. The existence of the uncertainty limits an application of the traditional probability model for the fault diagnosis technology of bearings [3,4]. An interval model is one of the common methods to describe the uncertainty of bearing signals, but it can obtain only the range of the bearing data, not its probability distribution, and the statistical information of the bearing data cannot be perfectly used.…”

Section: Introductionmentioning

confidence: 99%

Tapered Roller Bearing Failure Diagnosis Based on Improved Probability Box Model

Tang

Dai

2020

IEEE Access

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Bearing failure often occurs in rotating machinery. The fault diagnosis method based on the vibration signals has been studied for many years. This paper proposed an improved probability box (ip-box) modeling method for diagnosing bearing faults. The major theoretical principles involved with the probability box (p-box) modeling methods and a projection method. Since a larger aggregated width results in the p-box not being conducive to a fault identification and diagnosis, the mean of the focal element interval and the amount of data fluctuation between the adjacent focal elements were used as additional information. Then, the additional information was added to the ip-box model by the cooperative optimization method. Finally, the experimental results showed that the classification performance of a support vector machine (SVM) trained with eight measured values from the ip-box was significantly improved.

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Requirements to Data Acquisition and Signal Analysis for Electrical Grid Condition Monitoring

Anijarv

Shabbir

Kütt

et al. 2020

2020 IEEE 61th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)

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Multi-resolution wavelet analysis for noise reduction in impulse puncture voltage measurements

Cited by 3 publications

References 17 publications

Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study

Tapered Roller Bearing Failure Diagnosis Based on Improved Probability Box Model

Requirements to Data Acquisition and Signal Analysis for Electrical Grid Condition Monitoring

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