An experimental method for diagnostic of incipient broken rotor bar fault in induction machines

Sabir, Hamza; Ouassaid, Mohammed; Ngote, Nabil

doi:10.1016/j.heliyon.2022.e09136

Cited by 15 publications

(7 citation statements)

References 46 publications

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“…Researchers Sabir et al [11] presented a new experimental technique for the diagnosis of incipient faults in induction motor rotors based on the analysis of the linear stator current, highlighting six critical aspects for detection and classification. Despite the promising results of early diagnosis without direct access to the machine, there may be limitations regarding the generalizability of the method for different fault types and operating conditions.…”

Section: Related Workmentioning

confidence: 99%

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Diagnostics of Damage to the Rotor of an Induction Machine Using the Method of Instantaneous Power Analysis

Puka,

Bardhi,

Pjetri

et al. 2024

QAJ

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The relevance of the problem under study is that in the modern world, where induction machines are widely used in industry, early detection of rotor damage using the method of instantaneous power analysis is critical to maintaining uninterrupted equipment operation and reducing operating costs. The purpose of this study is to investigate and determine the effectiveness of the method of instantaneous power analysis for diagnosing rotor damage in induction machines to improve their reliability and reduce the risk of production failures. The methods used include the analytical method, classification method, functional method, statistical method, synthesis method. In today’s industry, asynchronous machines play a central role, creating a high demand for their reliable and safe operation. Malfunctions and failures in the operation of these machines can lead to serious downtime and considerable losses that affect the financial position of the company. One method of accurate fault diagnosis is to analyses the theoretical components added to the motor current. The analysis revealed that the locus of the space vector, instantaneous power, and sideband frequencies around the fundamental frequency indicate possible broken rotor cores of an induction machine. These components become key indicators for fault detection in induction machines. Furthermore, the experiments on a three-phase squirrel-cage induction confirmed the theoretical analysis, reinforcing the practical significance of the developed methodology for early diagnosis and improving equipment reliability, as well as reducing maintenance and repair costs. The practical significance lies in the development of a methodology that can be used in the industry for early diagnosis of rotor damage in induction machines, ensuring increased equipment reliability and reduced maintenance and repair costs.

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Section: Related Workmentioning

confidence: 99%

“…The interaction of magnetic flux and the rotor bar currents generates an electromagnetic torque. The space vector of voltage and currents of stator winding also the space vector of rotor currents according stator reference frame can be expressed as follows (9)(10)(11):…”

Section: A Characterization Of Induction Motorsmentioning

confidence: 99%

Diagnostics of Damage to the Rotor of an Induction Machine Using the Method of Instantaneous Power Analysis

Puka,

Bardhi,

Pjetri

et al. 2024

QAJ

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“…2.1.2 Rotor voltage equation Figure 3 represents the equivalent electric circuit of a mesh of the rotor cage, where the rotor bars and the portions of shortcircuit rings are represented by their resistances and corresponding leakage inductances [12].…”

Section: Stator Voltage Equationmentioning

confidence: 99%

Learning Machine Based on Optimized Dimensionality Reduction Algorithm for Fault Diagnosis of Rotor Broken Bars in Induction Machine

Fares¹,

Zoubir²,

Thlaijia³

et al. 2022

EJEE

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Induction machine health monitoring is considered a developing technology for the online detection of faults that occur even at the initial stage. The objective of this study is to present an artificial intelligence (AI) technique for the detection and localization of adjacent and distant broken bar faults in the induction machine, through a multi-winding model for the simulation of these cases. In this work, it was found that the application of Artificial Neural Networks (ANN) based on Mean Squared Error (MSE) and Random Forest (decision tree) plays an important role in detecting and locating defaults. The stator current signal Ias of a motor in the dynamic state was acquired from a healthy and faulty motor with a broken rotor bar fault. 9 statistical features and 8 wavelet packet parameters are extracted from the stator current signal. These features were employed as an input vector to train and test the ANN and random fores29t and determine whether the motor was running under normal conditions or defective. For optimizing the rotor bar defect classification procedure, feature selection algorithms are adopted, such as BBAT and BPSO. For feature reduction, we used the principal component analysis (PCA) algorithm, to reduce the number of features. The results showed that the random forest classifier based on statistical parameters and wavelet packet parameters followed by PCA can detect the defective with high accuracy (98.3333%) compared to other methods.

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“…Aydin et al, applied Hilbert transform and MCSA (motor current signature analysis) techniques to the stator current signal to determine the number of broken rotor bars [6]. Sabir et al, using DWT and ETSA (electrical time synchronous averaging) from the current signals obtained from the rotor failure experiment set they created, found the rotor failure frequency range and classified the failure with Fuzzy Logic [7]. Bessam et al created a broken rotor bar failure test set and performed tests at different loads in order to determine the number of broken rotor bars.…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors

DİŞLİ

Gedikpınar

Şengür

2023

Turkish Journal of Science and Technology

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Modern industrial drives use induction motors (IM) due to their starting and running torque requirements and four-quadrant operation. The voltages and currents of each of the three phases as well as the acceleration and velocity signals may be used to identify failures in the rotor bars of the motor. In the past, traditional signal processing-based feature extraction approaches and machine learning algorithms have been used for the diagnosis of the number of broken rotor bars for a failed IM. In this paper, a new scheme is investigated for the determination of the number of broken rotor bars. Specifically, the deep learning approaches are examined for the mentioned purposes. To this end, transfer learning approaches on the convolutional neural network (CNN) are presented. For denoising, initially, a bandpass filter is employed, and then the signals are converted to time-frequency images by using the continuous wavelet transform (CWT). The obtained images are used for the fine-tuning of the pre-trained ResNet18 model and the deep feature extraction and classification with the support vector machine (SVM) classifier. The classification accuracy is used for performance evaluation metrics. Moreover, the experiments show that the highest accuracy score of 100% is obtained with the deep features that are extracted from the mechanical vibration signal and current signal. On the other hand, a performance comparison with the published approaches is also carried out. Finally, the comparisons show that the proposed method produces better accuracy scores than the compared methods.

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An experimental method for diagnostic of incipient broken rotor bar fault in induction machines

Cited by 15 publications

References 46 publications

Diagnostics of Damage to the Rotor of an Induction Machine Using the Method of Instantaneous Power Analysis

Diagnostics of Damage to the Rotor of an Induction Machine Using the Method of Instantaneous Power Analysis

Learning Machine Based on Optimized Dimensionality Reduction Algorithm for Fault Diagnosis of Rotor Broken Bars in Induction Machine

Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction Motors

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