Diagnosis of rotor asymmetries in induction motors based on the transient extraction of fault components using filtering techniques

Riera-Guasp, M.; Antonino-Daviu, José A.; Rusek, J.; Roger‐Folch, J.

doi:10.1016/j.epsr.2009.02.009

Cited by 23 publications

(10 citation statements)

References 32 publications

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“…They provide a sensitive means to diagnose the faults in comparison to other signal processing methods like the Fourier Transform, the drawbacks of which include the need to use a single window function in all frequency components and the acquisition of linear resolution in the whole frequency domain. This is an important reason for the interest in wavelets in time-frequency analysis as can be seen in (M. Riera-Guaspa et al, 2009). (Andrew K.S.…”

Section: Discrete Wavelet Transform Fault Detectionmentioning

confidence: 99%

“…(Gang Niu et al, 2008) employed Bayesian belief fusion and multi agent fusion as a classifier tool to detect different faulty collected data using the signal processing techniques for smoothing and then used DWT to decompose the signals into different ranges of frequency. Detection and diagnosis for rotor asymmetries in the induction motor based on the analysis of the stator start-up current has been done by (M. Riera-Guaspa et al, 2009). The authors extracted the harmonic component introduced by this fault.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wavelet Fault Diagnosis of Induction Motor

Gaeid¹,

Ping²

2011

MATLAB for Engineers - Applications in Control, Electrical Engineering, IT and Robotics

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Section: Discrete Wavelet Transform Fault Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wavelet Fault Diagnosis of Induction Motor

Gaeid¹,

Ping²

2011

MATLAB for Engineers - Applications in Control, Electrical Engineering, IT and Robotics

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“…As is clear in the figure, the starting time trend increased based on increment in the load. A recent study performed on a squirrel-cage induction motor revealed that the startup load is not important in BRB fault detection based on the transient analysis; however, they mentioned that this approach is especially suitable for applications with heavy startup transients [34]. Other researchers also did not consider the effects of starting load for fault detection in induction machines based on the analysis of current in transient state [35,36].…”

Section: Performance Of Ls-pmsm With Presence Of Faultmentioning

confidence: 99%

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

et al. 2017

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High-efficiency motors are being gradually introduced in many industrial applications because of their positive impacts on the environment by reducing energy consumption and CO 2 emission. In this respect, line start permanent magnet synchronous motors (LS-PMSMs) have been introduced recently. Due to their unique configuration, LS-PMSMs allow the obtaining of super premium efficiency levels, accompanied with a high torque and power factor. However, since the use of LS-PMSMs in the industry is in its infancy, no efficient scheme has yet been reported for broken rotor bar (BRB) fault detection in this type of motor. Accordingly, the main aim of this research is to investigate the fault-related feature for BRB faults on LS-PMSMs. In this regard, a simulation model and experimental setup for the investigation of BRB in LS-PMSM are implemented. The detection strategy for BRB in LS-PMSM proposed here is based on the monitoring of the start-up current signal and discrete wavelet transform. The entropy features are used as fault-related features for BRB faults. Finally, the ability of these features is validated for the detection of BRB in LS-PMSM through statistical analysis. In this research, the importance of the starting load is also considered for BRB detection in LS-PMSMs.

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“…For multi-component and non-stationary stator currents, a filtering stage is required if the modes are separable [34]. Otherwise, techniques such as EMD and EEMD are required in order to extract mono-component signals (called IMFs) from the stator currents [35][36].…”

Section: Introductionmentioning

confidence: 99%