Wavelet Fault Diagnosis of Induction Motor

Gaeid, Khalaf Salloum; Ping, Hew Wooi

doi:10.5772/19584

Cited by 8 publications

(9 citation statements)

References 54 publications

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“…With the Fault Tolerant Control, it is intended to detect incipient failures in sensors and/or actuators, and quickly adapt the system for its proper functioning. In the review ar ticle [57], a large number of investigations are presented which have implemented WT for fault-tolerant control systems. In this article it is concluded, from the analysis of the researches reviewed, that Wavelet provides an effective method for the diagnosis of faults in comparison with other methods of signal processing.…”

Section: Applications Of Wavelet Transform To Induction Motor Analysismentioning

confidence: 99%

Wavelet transform to induction motor analysis: review

2018

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Section: Applications Of Wavelet Transform To Induction Motor Analysismentioning

confidence: 99%

Wavelet transform to induction motor analysis: review

2018

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“…The method of wavelet analysis includes a discussion of different wavelet functions and gives details for the analysis of the wavelet power spectrum (Salloum and Ping, 2011). A wavelet is a function ψ belonging to L 2 (R) with a zero average.…”

Section: Discrete Wave Transformmentioning

confidence: 99%

“…The discrete Haar transform consists of functions on a matrix row-wise finding the sums and differences of consecutive elements (Salloum and Ping, 2011). While each Haar function contains just one wavelet during some subinterval of time and remains zero elsewhere, the Haar set forms a local basis.…”

Section: Haar Waveletmentioning

confidence: 99%

A Real Time Data Acquisition and Vibration Analysis through Wavelet Transform for Fault Detection of Industrial Drives

Jayakumar¹,

Thangavel²

2015

RJASET

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Prevention is better than cure is not only applicable to medical domain but also equally applicable to engineering field. By analyzing the symptoms and taking appropriate corrective measures before the actual failure takes place would avoid costly cateroscopic damages. In line with this approach a fault analysis through vibration study with wavelet transforms has been attempted in this study. The previous researchers adopted a wavelet analysis for stator fault, such as stator current signature analysis. But the present study had adopted frequency pattern using the decomposition of wavelet pocket for induction machine fault analysis. Subsequently the machine would be determined by analyzing the data obtained through HAAR wavelet. Induction motor, one with good condition and another with two broken ball bearing were taken up for fault analysis using above techniques. The proposed investigation of vibration analysis is done through frequency pattern using the decomposition of wavelet packet. The wavelet approximate and detailed coefficients for the vibration signal have been extracted over a wide range and the analysis could be percolated on the frequency domain. In this study, healthy and unhealthy motor are used to experimentally observe the vibration signals are transformed into power spectral density through Matlab tools and the steady state rotor frequency was introduce with a new frequency pattern for fault diagnosis.

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“…Over the past two decades, numerous studies have focused on broken rotor bars faults detection and analysis. References 10 and 11 present the WT role for stator current signals of an induction motor analysis with squirrel cage linked of broken rotor bars faults in starting or transient regime. In Reference 12, the WT is used to detect broken rotor bars and short‐circuit ring portions based on fact that any failure at the rotor, this is induces a deformation in air gap magneto‐motive force ( MMF ) which results in additional components in the stator currents.…”

Section: Introductionmentioning

confidence: 99%

A new transform discrete wavelet technique based on artificial neural network for induction motor broken rotor bar faults diagnosis

Defdaf

Berrabah

Chebabhi

et al. 2021

Int Trans Electr Energ Syst

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Summary The main objective of this article is to contribute the automatic fault diagnosis of broken rotor bars in three‐phase squirrel‐cage induction motor using vibration analysis. In fact, two approaches are combined to do so, based on signal processing technique and artificial intelligence technique. The first technique is based on discrete wavelet transform (DWT) to detect the harmonics that characterize this fault, using the Daubechies wavelet vibration analysis according to three axes (X, Y, Z). This application permits having the approximation mode function and the details (recd). To exact choice of reconstruction details which contains the information of the broken rotor bars faults, two statistical studies based on the root mean square values (RMS) and Kurtosis shock factor calculation are carried out for each (recd). The choice of (recd) is conditioned by (RMS) and Kurtosis values as: RMSrecd1 < RMSrecd2 and Kurtosisrecd1 > Kurtosisrecd2. Experimental results showed that (recd1 and recd2) satisfied the condition set for (RMS) and Kurtosis values. At the end of first technique, a spectral envelop of recd1 is adopted to detect the broken rotor bars fault and the second technique based on artificial neural network (ANN) is used to identify the number of broken rotor bars. The characteristics of features used as input variables of ANN are the RMS of recd1 and recd2, and the Kurtosis shock factor of recd1 and recd2. The experimental results demonstrated the high efficiency of the proposed method with rotor broken bars fault classification rate of 98.66%.

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Wavelet Fault Diagnosis of Induction Motor

Cited by 8 publications

References 54 publications

Wavelet transform to induction motor analysis: review

Wavelet transform to induction motor analysis: review

A Real Time Data Acquisition and Vibration Analysis through Wavelet Transform for Fault Detection of Industrial Drives

A new transform discrete wavelet technique based on artificial neural network for induction motor broken rotor bar faults diagnosis

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