A survey on time and frequency characteristics of induction motors with broken rotor bars in line-start and inverter-fed modes

Ghorbanian, Vahid; Faiz, Jawad

doi:10.1016/j.ymssp.2014.08.022

Cited by 66 publications

(79 citation statements)

References 103 publications

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“…Furthermore, as the validity of the method at rated frequency has been demonstrated in the theoretical explanation and in the case of study, in the following tests (inverter-fed tests), the IM has been tested at lower frequencies to prove also its validity under these conditions, usual in the case of inverted-fed induction motors. The use of both control methods CV/F (open loop) and DTC (close loop) has been considered of interest due to the fact that, as [34] points out, close-loop controls damps the speed variations due to the fault (2sf ), unlike open-loop controls. Anyway, regardless of the control method, the electromagnetic fault component always appears in the spectrum of the current and in the spectrum of the current envelope.…”

Section: Experimental Validation Of the Proposed Methodsmentioning

confidence: 99%

Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current

Sapena-Bañó

Pineda-Sánchez

Puche-Panadero

et al. 2015

IEEE Trans. Energy Convers.

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Fault diagnosis of rotor asymmetries in induction machines working at a very low slip, through Fourier-based methods, usually requires a long acquisition time to achieve a high spectral resolution and a high sampling frequency to reduce aliasing effects. However, this approach generates a huge amount of data, which makes its implementation difficult using embedded devices with small internal memory, such as digital signal processors and field programmable gate arrays or devices with low computing power. In this paper, a new simplified diagnostic signal designated as the reduced envelope of the stator current is introduced to address this problem. The reduced envelope signal is built using only one sample of the current per cycle without any further processing, and it is demonstrated that it carries the same spectral information about the fault as the full-length current signal. Based on this approach, an embedded device has only to store and process a minimal set of samples compared with the raw current signal for a desired resolution. In this paper, the theoretical basis of the proposed method is presented, as well as its experimental validation using two different motors with broken bars: 1) a high-power induction motor working in a factory; and 2) a low-power induction motor mounted in a laboratory test bed.Index Terms-Broken rotor bar detection, condition monitoring, digital signal processor (DSP), fault diagnosis, field-programmable gate array (FPGA), Fourier transforms, Hilbert transforms (HTs), induction motor, motor-current signature analysis, signal processing. 0885-8969

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Section: Experimental Validation Of the Proposed Methodsmentioning

confidence: 99%

Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current

Sapena-Bañó

Pineda-Sánchez

Puche-Panadero

et al. 2015

IEEE Trans. Energy Convers.

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“…We used random forests for the detection of rotor faults, and assessed the effectiveness of random forests by comparing with a decision tree, a Naïve Bayes classifier, logistic regression, linear ridge, and a support vector machine. To train these machine learning algorithms, we extracted thirteen time domain features from the transient current signal of the LS-PMSM, with the selection of the features based on prior work [22,23]. While machine learning methods have been used for fault detection in induction motors [7][8][9][10][11], to the best of our knowledge this is the first work to present fault analysis in LS-PMSMs by comparing various machine learning algorithms and using features extracted from the transient current signal to train and test these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fault detection of broken rotor bar in LS-PMSM using random forests

et al. 2018

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Abstract-This paper proposes a new approach to diagnose broken rotor bar failure in a line start-permanent magnet synchronous motor (LS-PMSM) using random forests. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13 statistical time domain features from the startup transient current signal, and used these features to train and test a random forest to determine whether the motor was operating under normal or faulty conditions. For feature selection, we used the feature importances from the random forest to reduce the number of features to two features. The results showed that the random forest classifies the motor condition as healthy or faulty with an accuracy of 98.8% using all features and with an accuracy of 98.4% by using only the mean-index and impulsion features. The performance of the random forest was compared with a decision tree, Naïve Bayes classifier, logistic regression, linear ridge, and a support vector machine, with the random forest consistently having a higher accuracy than the other algorithms. The proposed approach can be used in industry for online monitoring and fault diagnostic of LS-PMSM motors and the results can be helpful for the establishment of preventive maintenance plans in factories.

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“…MCSA allows noninvasive fault 2 Shock and Vibration diagnosis online [4]. This technique uses a Hall-effect sensor to measure signals of stator current and a data acquisition system to acquire the signal [5].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient

Delgado-Arredondo¹,

García-Pérez²,

Moriñigo-Sotelo

et al. 2015

Shock and Vibration

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Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different timefrequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC), and fast Fourier transform (FFT). The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.

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A survey on time and frequency characteristics of induction motors with broken rotor bars in line-start and inverter-fed modes

Cited by 66 publications

References 103 publications

Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current

Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current

Fault detection of broken rotor bar in LS-PMSM using random forests

Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient

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