New Spectral Markers for Broken Bars Diagnostics in Induction Motors

Baranov, Georgii D.; Nepomuceno, Erivelton G.; Vaganov, Michail A.; Ostrovskii, Valerii Y.; Butusov, Denis N.

doi:10.3390/machines8010006

Cited by 9 publications

(3 citation statements)

References 22 publications

(42 reference statements)

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“…Developing a reliability model for electrical drive systems requires a well-documented database of motor failures on which to base failure predictions or numerical models of the equipment [54]. Therefore, a large number of studies have focused precisely on investigating this area [48,[55][56][57][58][59]. The aging of energy infrastructure is a global challenge, so the scientific search for and active action in technologies' improvement that is capable of affecting rates of depreciation and reducing operating costs will give a significant boost not only to the oil and gas industry, but to all sectors [60][61][62].…”

Section: Methodsmentioning

confidence: 99%

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

2023

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Among the most widespread systems in industrial plants are automated drive systems, the key and most common element of which is the induction motor. In view of challenging operating conditions of equipment, the task of fault detection based on the analysis of electrical parameters is relevant. The authors propose the identification of patterns characterizing the occurrence and development of the bearing defect by the singular analysis method as applied to the stator current signature. As a result of the decomposition, the time series of the three-phase current are represented by singular triples ordered by decreasing contribution, which are reconstructed into the form of time series for subsequent analysis using a Hankelization of matrices. Experimental studies with bearing damage imitation made it possible to establish the relationship between the changes in the contribution of the reconstructed time series and the presence of different levels of bearing defects. By using the contribution level and tracking the movement of the specific time series, it became possible to observe both the appearance of new components in the current signal and the changes in the contribution of the components corresponding to the defect to the overall structure. The authors verified the clustering results based on a visual assessment of the component matrices’ structure similarity using scattergrams and hierarchical clustering. The reconstruction of the time series from the results of the component grouping allows the use of these components for the subsequent prediction of faults development in electric motors.

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Section: Methodsmentioning

confidence: 99%

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

2023

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“…Given that the initial angular position can vary depending on the angular reference point and the position of the broken bars, we proposed generating new images representing different combinations that can occur when a bar breaks [38]. Adjusting the initial angular position by shifting it from 0 to 90 degrees in 1-degree intervals is suggested.…”

Section: Image Datasetmentioning

confidence: 99%

A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors

Barrera-Llanga,

Burriel-Valencia,

Sapena-Bañó

et al. 2023

Sensors

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Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs.

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“…The sideband elements of the current spectrum are extracted and examined to demonstrate rotor flaws. A method to identify BRB frequencies from the spectrum in IMs using the MCSA approach was shown in [14]. The experimental and simulation findings of rotor bars that had deteriorated were reviewed.…”

Section: Introductionmentioning

confidence: 99%

Rotor Bar Fault Diagnosis in Indirect Field–Oriented Control-Fed Induction Motor Drive Using Hilbert Transform, Discrete Wavelet Transform, and Energy Eigenvalue Computation

Indragandhi

Aljafari

et al. 2023

Machines

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The most powerful technology in the condition-based maintenance (CBM) framework for rotating machinery is fault detection (FD) and fault diagnosis (FDS). This paper investigates the broken rotor bar (BRB) FDS utilizing Hilbert transform (HT), discrete wavelet transform (DWT), and energy eigenvalue (EEV) computation with the induction motor (IM) drive handled by the indirect field orientation control (IFOC). The stator current spectrum, which the HT collects, is utilized to determine BRB degradation. The DWT decomposes the signal while the fast Fourier transform (FFT) recovers the signal’s frequency and amplitude factors. The EEV of the motor current in the signal determines the degree of the malfunction and provides a better method for recognizing errors. The DWT is used to overcome the Fourier analysis’s drawbacks and is primarily dedicated to non-stationary signals. While DWT is used, the malfunctioning BRB’s stator current signal is restrained from its original amplitude. The results demonstrate that the proposed method can identify and diagnose faults in an IM drive even under different loads.

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New Spectral Markers for Broken Bars Diagnostics in Induction Motors

Cited by 9 publications

References 22 publications

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors

Rotor Bar Fault Diagnosis in Indirect Field–Oriented Control-Fed Induction Motor Drive Using Hilbert Transform, Discrete Wavelet Transform, and Energy Eigenvalue Computation

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