On-line Faults Signature Monitoring Tool for Induction Motor Diagnosis

Medoued, Ammar; Lebaroud, Abdesselem; Boukadoum, Ahcene; Carrault, Guy

doi:10.5370/jeet.2010.5.1.140

Cited by 8 publications

(3 citation statements)

References 8 publications

(2 reference statements)

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“…Thus the success of condition is based on maintenance and moreover it is having an accurate conditional assessment and fault diagnosis. The illustration also shows a block of on-line conditional monitoring system/subsystem for proposed mechanism (Medoued et al, 2010). Sensors can be employed to measure up the signals that detect the faults zone.…”

Section: Induction Motor Faultmentioning

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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Section: Induction Motor Faultmentioning

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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“…In the litterature, various methods are used for fault diagnosis of the induction motors, among them, we can cite: Motor Current Signature Analysis (MCSA) (Lebaroud and Medoued, 2013; Medoued et al, 2010), signal processing–based methods, wavelet analysis (Ordaz-Moreno et al, 2008), time–frequency representations (Lebaroud and Clerc, 2008; Medoued et al, 2014; Medoued et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis in yaw drive induction motor for wind turbine

et al. 2018

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Wind turbines are widely exploited throughout the world. The availability and reliability of offshore wind turbines are asking to impose a constant maintenance strategy. In this work, we propose a method that allows filtering the signal of the frequency inverter that feeds the yaw drive used in wind turbine. The redundant information is eliminated via discrete wavelet transform and empirical modal decomposition. The two types of faults are detected from the envelope of the Hilbert transform. The magnitude imbalance detection is carried out in the time domain. The root mean square values of the envelopes of the threephase system have a good indicator for the fuzzy system to evaluate the severity of the defect. In the frequency domain, the signature of the broken bar fault is located in the low-frequency bandwidth. The harmonics appeared in the spectrum sensitive in amplitude and frequency to the variation of load. Experimental results have demonstrated the accuracy of the proposed method.

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“…Nowadays, maintenance cost reductions are the number one priority for electrical drive to prevent unscheduled downtimes and to increase operational effectiveness. Recent advances of signal processing techniques, such as artificial neural networks [3][4][5][6][7][8], wavelets [9], etc.., have provided more powerful tools for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Classification of Induction Machine Faults using Time Frequency Representation and Particle Swarm Optimization

Medoued¹,

Lebaroud²,

Laïfa³

et al. 2014

Journal of Electrical Engineering and Technology

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-This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

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On-line Faults Signature Monitoring Tool for Induction Motor Diagnosis

Cited by 8 publications

References 8 publications

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

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

Fault diagnosis in yaw drive induction motor for wind turbine

Classification of Induction Machine Faults using Time Frequency Representation and Particle Swarm Optimization

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