Detection of broken rotor bars of induction motors based on the combination of Hilbert envelope analysis and Shannon entropy

Kabul, Ahmet; Ünsal, Abdurrahman

doi:10.1515/teme-2020-0066

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…where 𝑠 is the slip. In the envelope spectrum, the characteristic harmonic component of broken rotor bars is calculated employing 𝑓 𝑠 − 𝑓 𝑏𝑟 𝑏 [30].…”

Section: Analysis Of Stator Current Signals (Case 1)mentioning

confidence: 99%

Metrology and Measurement Systems

Kabul,

Ünsal

2021

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Three phase induction motors are widely used in industrial processes and condition monitoring of these motors is especially important. Broken rotor bars, eccentricity and bearing faults are the most common types of faults of induction motors. Stator current and/or vibration signals are mostly preferred for the monitoring and detection of these faults. Fourier Transform (FT) based detection methods analyse the characteristic harmonic components of stator current and vibration signals for feature extraction. Several types of simultaneous faults of induction motors may produce characteristic harmonic components at the same frequency (with varying amplitudes). Therefore, detection of multiple faults is more difficult than detection of a single fault with FT based diagnosis methods. This paper proposes an alternative approach to detect simultaneous multiple faults including broken rotor bars, static eccentricity and outer/inner-race bearing faults by analysing stator current and vibration signals. The proposed method uses Hilbert envelope analysis with a Normalized Least Mean Square (NLSM) adaptive filter. The results are experimentally verified under 25%, 50%, 75%, 100% load conditions.

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“…where 𝑠 is the slip. In the envelope spectrum, the characteristic harmonic component of broken rotor bars is calculated employing 𝑓 𝑠 − 𝑓 𝑏𝑟 𝑏 [30].…”

Section: Analysis Of Stator Current Signals (Case 1)mentioning

confidence: 99%

Metrology and Measurement Systems

Kabul,

Ünsal

2021

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“…During fault diagnosis, human factors interfere greatly, resulting in poor accuracy of diagnosis results and complex diagnosis process. Intelligent fault diagnosis technology exhibits glaring advantages over conventional fault detection techniques [9,10]. Based on deep learning, building a fault diagnosis model by learning historical fault data has been widely concerned [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Stator-rotor fault diagnosis of induction motor based on time-frequency domain feature extraction

Yi,

Long,

Wang

et al. 2023

Metrology and Measurement Systems

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Since the induction motor operates in a complex environment, making the stator and rotor of the motor susceptible to damage, which would have significant impact on the whole system, efficient diagnostic methods are necessary to minimize the risk of failure. However, traditional fault diagnosis methods have limited applicability and accuracy in diagnosing various types of stator and rotor faults. To address this issue, this paper proposes a stator-rotor fault diagnosis model based on time-frequency domain feature extraction and Extreme Learning Machine (ELM) optimized with Golden Jackal Optimization (GJO) to achieve highprecision diagnosis of motor faults. The proposed method first establishes a platform for acquiring induction motor stator-rotor fault data. Next, wavelet threshold denoising is used to pre-process the fault current signal data, followed by feature extraction to perform time-frequency domain eigenvalue analysis. By comparison, the impulse factor is finally adopted as the feature vector of the diagnostic model. Finally, an induction motor fault diagnosis model is constructed by using the GJO to optimize the ELM. The resulting simulations are carried out by comparing with neural networks, and the results show that the proposed GJO-ELM model has the highest diagnostic accuracy of 94.5%. This finding indicates that the proposed method outperforms traditional methods in feature learning and classification of induction motor fault diagnosis, and has certain engineering application value.

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“…The frequency location of these components is associated with the slip s and the fundamental frequency f s . The severity of BRB is indicated by the magnitude of the fault component [4][5][6]. In Fourier transform (FT) based MCSA, the spectrum of stator currents can be used to diagnose BRB faults in motors.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Diagnosis of Broken Bars Based on Optimum Resolution of Prescient Direction Algorithm

Jia

Wang

Zhu

et al. 2022

Wireless Communications and Mobile Computing

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Fourier transformation (FT) and Multiple Signal Classification (MUSIC) method suffer the insufficient ability in diagnosing broken rotor bar (BRB) fault using short-time data. Theoretical and simulation analyses show that the Optimum Resolution of Prescient Direction (ORPD) algorithm has the best frequency resolution performance due to a priori knowledge. The main objective of this paper is to detect BRB faults in induction machines using a condition monitoring architecture based on ORPD algorithm. In the proposed application, the ORPD algorithm with the best frequency resolution performance is used to estimate the fault-sensitive frequencies in the stator current signature. The prior information of BRB fault characteristic distribution is used to construct a weighting matrix in the ORPD algorithm, for acquiring the lowest signal-to-noise ratio resolution threshold. Once frequencies are estimated, their corresponding amplitudes are obtained by using the least squares estimator. The proposed methods were tested using experimental induction motors with different fault severity under the effect of several load levels or supply frequencies. Two types of power supply modes are considered: main and inverter. The results show that compared with traditional method which uses FT and MUSIC algorithm for fault diagnosing, the method based on ORPD algorithm has a higher frequency resolution and identification ability with short-time data, and still has good diagnostic performance even under light loading and lower supply frequencies.

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Detection of broken rotor bars of induction motors based on the combination of Hilbert envelope analysis and Shannon entropy

Cited by 7 publications

References 40 publications

Metrology and Measurement Systems

Metrology and Measurement Systems

Stator-rotor fault diagnosis of induction motor based on time-frequency domain feature extraction

A Novel Method for Diagnosis of Broken Bars Based on Optimum Resolution of Prescient Direction Algorithm

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