Spectral Analysis Algorithm for the Diagnosis of Electrical Machines

Baranov, Georgii D.; Matus, Konstantin I.; Vaganov, Michail A.; Bubnov, Evgeny A.

doi:10.1109/eiconrus.2019.8657020

Cited by 2 publications

(5 citation statements)

References 1 publication

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“…Energies 2021, 14, x FOR PEER REVIEW 4 of 24 As shown in Figure 1, the compensated type Hall-effect current sensors and the energy parameter control sensors on the individual power supply line of each motor are removed, and the energy parameter control sensors on the general power flow are installed. The results of statistical analysis searching for the causes of induction motor [28,29] failures show that the main types of defects, considered as bearing wear, air gap eccentricity, turn-to-turn faults and breakage of rotor rods are described by harmonic components of current [30,31]. It should be noted that turn-to-turn short circuits are the most challenging due to the high rate of development and consequences (1):…”

Section: Methodsmentioning

confidence: 99%

Identification of the Technical Condition of Induction Motor Groups by the Total Energy Flow

2021

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The paper discusses the method of identifying the technical condition of induction motors by classifying the energy data coming from the main common power bus. The work shows the simulation results of induction motor operation. The correlation between occurring defects and current diagrams is presented. The developed simulation model is demonstrated. The general algorithm for conducting experiments is described. Five different experiments to develop an algorithm for the classification are conducted: determination of the motors number in operation with different power; determination of the motors number in operation with equal power; determination of the mode and load of induction electric motor; determination of the fault and its magnitude with regard to operation and load of induction motor; determination of the fault and its magnitude with regard to operation and load of induction motor with regard to non-linear load in the flow. The article also presents an algorithm for preprocessing data to solve the classification problem. In addition, the classification results are shown and recommendations for testing and using the classification algorithm on a real object are made.

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Section: Methodsmentioning

confidence: 99%

Identification of the Technical Condition of Induction Motor Groups by the Total Energy Flow

2021

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“…Next, we processed the resulting current with the spectral analysis and denoising algorithm (please see [22] for further details). This approach allows us to filter the white noise from the signal.…”

Section: Methodsmentioning

confidence: 99%

“…The spectrums that are given in Figure 6 and thereafter were obtained with the spectral analysis algorithm [22] as was stated before.…”

Section: Experimental Studymentioning

confidence: 99%

“…One can see from Figure 6a that the algorithm [22] extracted frequencies, specified their amplitudes, and filtered noise. You can also see that these frequencies were hidden by the side effects of the fast Fourier transform, e.g., this can be observed at 225 Hz.…”

Section: Experimental Studymentioning

confidence: 99%

“…The stator current spectrum when two rotor bars are broken, obtained by the method of: (a) Spectral analysis algorithm[22], (b) Fast Fourier transform.…”

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confidence: 99%

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New Spectral Markers for Broken Bars Diagnostics in Induction Motors

et al. 2020

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The paper discusses the spectral markers of fault rotor bars in induction motor current signature analysis (MCSA). The results of the simulation of the deterioration process for a single rotor bar, as well as the results of research for various mutual bracing of two broken bars, are reported. We proposed a simple empiric technique allowing one to obtain frequencies for spectrum markers of damaged rotor bars based on simulation analysis. The set of frequencies obtained in the experimental part of the study was compared with simulation results and the results of real-life measurements. The theoretical results were verified through the experiment with the real induction motor under load. Analysis of experimental results proved that the given algorithm for spectrum analysis is suitable for early detection of fault rotor bars in induction motors.

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Spectral Analysis Algorithm for the Diagnosis of Electrical Machines

Cited by 2 publications

References 1 publication

Identification of the Technical Condition of Induction Motor Groups by the Total Energy Flow

Identification of the Technical Condition of Induction Motor Groups by the Total Energy Flow

New Spectral Markers for Broken Bars Diagnostics in Induction Motors

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