FEM approach for diagnosis of induction machines' non‐adjacent broken rotor bars by short‐time Fourier transform spectrogram

Panagiotou, Panagiotis A.; Arvanitakis, Ioannis; Lophitis, Neophytos; Antonino-Daviu, Jose A.; Gyftakis, Konstantinos N.

doi:10.1049/joe.2018.8240

Cited by 14 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Firstly, the input current signal is decomposed by the CEEMD method into five IMFs, which are enough to depict the evolution of the characteristic left side band (LSB) frequency component during the startup transient of a motor with a broken bar, as can be seen in Figure 3 where the evolution of the LSB is highlighted by a dashed red line for each treated condition. These results are consistent with the ones obtained by the analytic and finite element (FE) models [15,38]. Evidently, there is no frequency information in the IMFs for an HLT condition.…”

Section: Proposed Methodology and Its Fpga Implementationsupporting

confidence: 89%

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

et al. 2019

View full text Add to dashboard Cite

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...

show abstract

Section: Proposed Methodology and Its Fpga Implementationsupporting

confidence: 89%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…e method based on process modeling usually establishes a mathematical model of faulty motor and converts the fault detection problem into the identification problem of corresponding parameters. For broken rotor bar (BRB) fault, the finite element method was used to establish IM models in [7][8][9][10][11] and the fault detection was realized by analyzing and comparing the changes of various physical quantities in the motor. e detection of BRB fault was realized by analyzing the changes of motor currents and gap flux density in [7].…”

Section: Introductionmentioning

confidence: 99%

“…e actual data and model data were compared to detect the BRB fault in [8,9]. e BRB fault detection was realized by monitoring the change of the external magnetic flux in [10].…”

Section: Introductionmentioning

confidence: 99%

Method for Identifying Stator and Rotor Faults of Induction Motors Based on Machine Vision

Lipeng

Xiang

Wang

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The detection results need to be analyzed and distinguished by professional technicians in the fault detection methods for induction motors based on signal processing and it is difficult to realize the automatic identification of stator and rotor faults. A method for identifying stator and rotor faults of induction motors based on machine vision is proposed to solve this problem. Firstly, Park’s vector approach (PVA) is used to analyze the three-phase currents of the motor to obtain Park’s vector ring (PVR). Then, the local binary patterns (LBP) and gray level cooccurrence matrix (GLCM) are combined to extract the image features of PVR. Finally, the vectors of image features are used as input and the types of induction motor faults are identified with the help of a random forest (RF) classifier. The proposed method has achieved high identification accuracy in both the Maxwell simulation experiment and the actual motor experiment, which are 100% and 95.83%, respectively.

show abstract

“…Therefore, several methodologies have been proposed to extract information about the motor condition relying on MCSA. One common technique for IM fault detection is the Fast Fourier Transform (FFT) [12,13], and its variations; for instance, the short-time Fourier transform (STFT) [14], the Gabor transform (GT) [15] and the method of selection of amplitudes of frequencies ratio 50 second frequency coefficient (MSAF-RATIO-50-SFC) [16,17], which are suitable for information extraction from non-stationary signals. However, the windowing length has a direct effect on the time and frequency resolution.…”

Section: Introductionmentioning

confidence: 99%

Automatic Early Broken-Rotor-Bar Detection and Classification Using Otsu Segmentation

et al. 2020

View full text Add to dashboard Cite

Induction motors (IM) are susceptible to mechanical failures with severe consequences for production lines; hence, detection and classification of IM faults have been of great interest for researchers in last years. Broken rotor bars (BRB) are one of the most difficult faults to detect, since this fault does not give any indication of deterioration increasing significantly the production costs; hence, it is quite important to detect them in early states. Several methodologies have been proposed to extract information about the motor condition relying on motor-current-signature analysis (MCSA); however, they usually require highcomputational-complexity algorithms to reach trustworthy result. In this work, a novel methodology for early detection and classification of BRB faults in IM is proposed. This methodology consists of obtaining two spectrograms using fixed-width windows, which are segmented through Otsu algorithm to visualize the time evolution of fault frequencies. The fault severity classification is performed through Kurtosis computation from non-stationary components. Obtained results from real experimentation validate the proposed-method high efficiency, reaching an overall 100% accuracy on detecting and classifying half, one, two BRBs, and healthy condition.

show abstract

FEM approach for diagnosis of induction machines' non‐adjacent broken rotor bars by short‐time Fourier transform spectrogram

Cited by 14 publications

References 17 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

Method for Identifying Stator and Rotor Faults of Induction Motors Based on Machine Vision

Automatic Early Broken-Rotor-Bar Detection and Classification Using Otsu Segmentation

Contact Info

Product

Resources

About