Diagnosis of bearing defects in induction motors using discrete wavelet transform

Bessous, Noureddine; Zouzou, S. E.; Bentrah, Wafa; Sbaa, Salim; Sahraoui, Mohamed

doi:10.1007/s13198-016-0459-6

Cited by 72 publications

(51 citation statements)

References 27 publications

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“…The Wavelet theory provides a unified framework for a number of techniques which have been developed for various signal processing applications. The DWT is based on the decomposition of the signal on a basis of particular functions [11,12]. In a variable speed control drives, the diagnosis is delicate, because the fault may appear as a disturbance for the control-loop, where the used Input-Output Linearization control corrects and compensates the fault effect, and unlike the field oriented control, the nonlinear control permits decoupling and linearizing the system without taking into account the flux orientation [13].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Diagnosis of the Inter-Turn Short Circuit Fault for the Sensorless Input-Output Linearization Control of the PMSM

Maanani

Menacer

2019

Period. Polytech. Elec. Eng. Comp. Sci.

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The purpose of this paper is the inter-turn short circuit fault Modeling and detection for the sensorless input-output linearization control of the permanent magnet synchronous motor (PMSM) based on the Extended Kalman Filter observer (EKF). The fault detection procedures are based through the estimation of the stator resistance variation by the Extended Kalman Filter observer and the Fast Fourier Transformer (FFT) for the stationary state, and the Discrete Wavelet Transform (DWT) analysis of the electrical characteristics of the PMSM, for the non-stationary state. However, the FFT spectral analysis and the DWT is a useful solution to ensure that the variation of the stator resistance estimation is caused by the inter-turn short circuit fault. The effectiveness of the sensorless control and the fault detection techniques are presented in a simulation in MATLAB/Simulink environment.

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Section: Introductionmentioning

confidence: 99%

Modeling and Diagnosis of the Inter-Turn Short Circuit Fault for the Sensorless Input-Output Linearization Control of the PMSM

Maanani

Menacer

2019

Period. Polytech. Elec. Eng. Comp. Sci.

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“…In this work, we present two methods for detection and isolation of the eccentricity faults in a three-phase variable speed squirrel cage induction motor using an indirect field oriented control (IFOC) using a fuzzy logic based speed controller in order to maintain good performances in degraded mode, that is, in the presence of eccentricity defects.The first method is based on fault detection with the evolution of components ids and iqs currents in the plan of Park. The second method is based on discrete wavelet decomposition (DWT) [14][15][16].…”

Section: Several Methods Have Been Developed For the Identification Omentioning

confidence: 99%

“…The plot of these values can be used to diagnose faults in the squirrel cage IM and it can also assess the severity degree of the fault. Before the calculation of the wavelet energy, the number of the decomposition levels must be well defined by the following relationship [16] [21,22]:…”

Section: Wavelet Energymentioning

confidence: 99%

Fault Eccentricity Diagnosis in Variable Speed Induction Motor Drive Using DWT

Reda¹,

fayçal²,

Bahi³

2017

AMA_C

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This paper describes the monitoring and diagnosis of eccentricity fault in variable speed induction motor drive. The used control technique is the indirect field oriented control (IFOC) fuzzy logic based controller to ensure the robust speed regulation and to compensate the fault effects. The variable speed induction motor can be affected by various kinds of faults. Airgap eccentricity is one of the major defects occurring in such electric drives; its detection could be useful for preventing potential catastrophic failures. A dynamic model taking into account the faults is proposed based on the approach of magnetically coupled coils to simulate the behavior of eccentricity faults in induction motor. This work presents two approaches for diagnosis and detection of eccentricity faults and evaluation of their severity based on monitoring of the stator current signals, using Park vector method and discrete wavelet transform (DWT) with different approaches to distinguish healthy as well as faulty conditions of the machine. The obtained simulation results via the proposed technique allow detection and diagnosis of eccentricity fault and identify their severity.

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“…The DWT implementation for digital computation, there is an efficient algorithm that is well-suited for MRA. The MRA decomposes the signal recursively with two-channel filter bank and down sampling process [11]…”

Section: F Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Signal Processing Methods for Identification of Induction Motor Bearing Fault

Agrawal*¹,

Tiwari²,

Giri³

2019

IJRTE

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To diagnose early faults as soon as possible, the feature extraction of vibration signals is very important in real engineering applications. Recently, the advanced signal processing-based weak feature extraction method has been becoming a hot research topic. The dominant mode of failure in rolling element bearings is spalling of the races or the rolling elements. Localized defects generate a series of impact vibrations every time whenever running roller passes over the surface of a defect. Therefore, vibration analysis is a conventional method for bearing fault detection. However, the measured vibration signals of rotating machinery often present nonlinear and non-stationary characteristics. This paper deals with the diagnosis of induction motor bearing based on vibration signal analysis. It provides a comparative study between traditional signal processing methods, such as Power Spectrum, Short Time Fourier Transform, Wavelet Transform, and Hilbert Transform. Performances of these techniques are assessed on real vibration data and compared for healthy and faulty bearing.

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Diagnosis of bearing defects in induction motors using discrete wavelet transform

Cited by 72 publications

References 27 publications

Modeling and Diagnosis of the Inter-Turn Short Circuit Fault for the Sensorless Input-Output Linearization Control of the PMSM

Modeling and Diagnosis of the Inter-Turn Short Circuit Fault for the Sensorless Input-Output Linearization Control of the PMSM

Fault Eccentricity Diagnosis in Variable Speed Induction Motor Drive Using DWT

Signal Processing Methods for Identification of Induction Motor Bearing Fault

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