Diagnostics of career dump truck traction induction motors technical condition using wavelet analysis

Большунова, О. М.; Kamyshian, A.; Bolshunov, A.

doi:10.1109/dynamics.2016.7818988

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…WT is a powerful means for analysing stationary and transient currents, voltages, and vibration in order to detect the presence of failure. DWT is the discrete version of WT and the most common transform employed in electrical engineering applications, particularly in monitoring systems for detection, localisation, and classification of the power system perturbations in time and frequency domains [13,17,18]. The DWT has become an effective tool in digital signal processing.…”

Section: Wavelet Decomposition and Energy Extractionmentioning

confidence: 99%

“…The obvious similarity in spectral analysis could lead to an inaccurate diagnosis. Due to these disadvantages, alternate approaches to diagnosis based on techniques, such as the Discrete Wavelet Transform (DWT) for the analysis of the stator current under wide frequency band, becomes acceptable in this situation [12,13]. A proposed works in [14,15], present an effective machine-learning-based fault diagnosis method, developed for induction motors driven by variable frequency drives (VFDs).…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we present a technique for diagnosing mechanical faults in induction machines. The method is contrasted with the standard decomposition in multi-levels via DWT of the stator current in a steady state, and additional steps are required to determine the energy associated with each level of decomposition [12,13]. The proposed energy estimation is used to analyse stator currents when the spectral content is distributed over a wide frequency band.…”

Section: Introductionmentioning

confidence: 99%

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Induction motor mechanical defect diagnosis using DWT under different loading levels

Bouzida¹,

Abdelli²,

Boudouda³

2023

Acta Polytech

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The information extraction capability of the widely used signal processing tool, FFT for diagnosing induction machines, is commonly used at a constant load or at different levels. The loading level is a major influencing factor in the diagnostic process when the coupled load and the machine come with natural mechanical imperfections, and at a low load, the mechanical faults harmonics are strongly influenced. In this context, the main objective of this work is the detection of the mechanical faults and the study of the effect of the loading level on the induction motor diagnostic process. We have employed a diagnosis method based on discrete wavelet transform (DWT) for the multi-level decomposition of stator current and extracting the fault’s energy stored over a wide frequency range. The proposed approach has been experimentally tested on a faulty machine with dynamic eccentricity and a shaft misalignment for three loading levels. The proposed method is experimentally tested and the results are provided to verify the effectiveness of the fault detection and to point out the importance of the coupled load.

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Section: Wavelet Decomposition and Energy Extractionmentioning

confidence: 99%