Broken Rotor Bar Detection in Induction Motors via Wavelet Ridge

Wei, Yunbing; Shi, Baohui; Cui, Guangzhao; Yin, Jun

doi:10.1109/icmtma.2009.510

Cited by 2 publications

(3 citation statements)

References 11 publications

(9 reference statements)

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“…In the last case, case 2, it means both the number of realizations is over and the calculation of a new "true IMF" is started. Mux A selects the signal R i (the difference between Xt i and the previous true IMF k i (8)) which is sent to be stored as X, Xa, and Xt. The whole process to calculate a new true IMF starts again by selecting in Mux 5 the values of Xa i + w i .…”

Section: Ceemd Modulementioning

confidence: 99%

“…In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.Mathematics 2019, 7, 783 2 of 19 been presented [8][9][10][11]. Wavelet transform (WT) is a windowing technique with variable regions in both time and frequency.…”

mentioning

confidence: 99%

“…Mathematics 2019, 7, 783 2 of 19 been presented [8][9][10][11]. Wavelet transform (WT) is a windowing technique with variable regions in both time and frequency.…”

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confidence: 99%

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Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

et al. 2019

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Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.Mathematics 2019, 7, 783 2 of 19 been presented [8][9][10][11]. Wavelet transform (WT) is a windowing technique with variable regions in both time and frequency. It increases time resolution at higher frequencies and frequency resolution at lower frequencies [12][13][14]. Although promising results have been obtained, several aspects have to be taken into account; for instance, WT performance can be affected under noisy conditions and adequate selection or choice of the level of decomposition and the mother wavelet have to be performed according to the input signal in order to carry out a suitable analysis [15].In order to avoid the configuration of different parameters in WT, recent works have presented the use of the empirical mode decomposition (EMD) method [16][17][18][19], which is an adaptive decomposition algorithm or method capable of evaluating non-linear and non-stationary signals. Due to the potential of the EMD method as a signal processing technique, several works have tried to make a hardware implementation for online processing. In [20], the EMD in a C program is processed by a field programmable gate array (FPGA). Another hardware implementation of EMD by fusing an FPGA and a digital signal processor (DSP) is proposed by [21]. The FPGA implementation of EMD using sawtooth transform instead of the spline cubic is presented in [22]. The EMD method using the spline cubic interpolation into an FPGA is presented in [23]. Recently,in [24], another design for EMD on an FPGA platform is presented, where the user can change different aspects of the implementation (e.g., data lengths, extrema extraction methods, envelope generation methods, and stopping criterion methods).Despite the great capabilities of the EMD method, several studies have shown that this method suffers from a problem named mode mixing [25], compromising the correct analysis of the modes of a signal. To overcome this problem, the ensemble empirical mode decomposition (EEMD) and, subsequently, an improved te...

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Section: Ceemd Modulementioning

confidence: 99%

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confidence: 99%

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Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

et al. 2019

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Broken bar fault diagnosis for induction machines under load variation condition using discrete wavelet transform

Zheng

Vagapov

et al. 2014

Proceedings of IEEE East-West Design &Amp; Test Symposium (EWDTS 2014)

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The paper presents a new approach for detection of broken rotor bar fault in squirrel cage induction motors operating under varying load conditions. A mathematical model used in the presented method was developed using winding function approach to provide indication references for induction motor parameters under load variation. The model shows a strong relationship between broken rotor bar fault and stator current. The method is based on analysis of stator current using discrete wavelet transform. To verify the proposed method a squirrel cage induction motor with 1, 2 and 3 broken bars at no-load, half-and full-load conditions was investigated. Obtained experimental results confirmed the validity of the proposed approach.

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Broken Rotor Bar Detection in Induction Motors via Wavelet Ridge

Cited by 2 publications

References 11 publications

Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

Broken bar fault diagnosis for induction machines under load variation condition using discrete wavelet transform

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