A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art

Liu, Yiqi; Bazzi, Ali M.

doi:10.1016/j.isatra.2017.06.001

Cited by 123 publications

(84 citation statements)

References 91 publications

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“…In this respect, condition monitoring techniques have continuously been developed over the years, resulting in a range of available methods for failure diagnosis in electrical devices. Condition monitoring techniques presented for broken rotor bar detection in induction machines can be classified into the following categories [4,5]: An LS-PMSM is made up of a stator and a hybrid rotor with a squirrel-cage and pairs of permanent magnets. In this machine, when the rotor is operated from a standstill, squirrel-cage bars generate a suitably high starting torque, enabling the direct-on-line movement of the rotor.…”

Section: Background and Methodologymentioning

confidence: 99%

Section: Background and Methodologymentioning

confidence: 99%

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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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Section: Background and Methodologymentioning

confidence: 99%

Section: Background and Methodologymentioning

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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“…In the literature, different artificial intelligence-based methods such as artificial neural networks (ANNs), fuzzy logic systems (FLSs), and support vector machines (SVMs), among others, for detecting broken rotor bars have been reported [41][42][43], with the ANNs being one of the most widely used methods [41]. ANNs are considered computing systems or models capable of simulating the neurological structure of the human brain for learning, classifying, and solving problems.…”

Section: Ffnn Modulementioning

confidence: 99%

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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“…The discussion on most of the methods used is included, among others, in [29,30]. To detect damage in cage rotors, the most commonly used methods are the Motor Current Signal Analysis (MCSA) and the Extended Park's Vector Analysis (EPVA) [29][30][31][32]. They are effective and feasible methods based on non-invasive measurement of the stator current.…”

Section: Introductionmentioning

confidence: 99%

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

et al. 2019

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Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values.

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A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art

Cited by 123 publications

References 91 publications

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

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

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

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

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