Broken Rotor Bar Detection in Induction Motors Based on Air-Gap Rotational Magnetic Field Measurement

Soleimani, Younes; Cruz, Sérgio M. A.; Haghjoo, Farhad

doi:10.1109/tim.2018.2870265

Cited by 34 publications

(31 citation statements)

References 29 publications

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“…this work, for early detection and classification of BRB in IM, regarding percentage load torque, processing time and classification effectiveness. Taking into account this comparison, the proposed methodology shows high accuracy in the detection and classification of broken rotor bars, reaching at least the same sensitivity and effectiveness than others approaches; although, it has been extensively proven that the higher the fault severity and/or the mechanical load, the easier the BRB detection [26,27], [36][37][38][39][40][41]. Furthermore, it is desirable to have a visual representation of the faultfrequency evolution in time, to corroborate the diagnosis of the induction motor operational condition.…”

Section: Effectiveness Validationmentioning

confidence: 89%

Automatic Early Broken-Rotor-Bar Detection and Classification Using Otsu Segmentation

et al. 2020

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Induction motors (IM) are susceptible to mechanical failures with severe consequences for production lines; hence, detection and classification of IM faults have been of great interest for researchers in last years. Broken rotor bars (BRB) are one of the most difficult faults to detect, since this fault does not give any indication of deterioration increasing significantly the production costs; hence, it is quite important to detect them in early states. Several methodologies have been proposed to extract information about the motor condition relying on motor-current-signature analysis (MCSA); however, they usually require highcomputational-complexity algorithms to reach trustworthy result. In this work, a novel methodology for early detection and classification of BRB faults in IM is proposed. This methodology consists of obtaining two spectrograms using fixed-width windows, which are segmented through Otsu algorithm to visualize the time evolution of fault frequencies. The fault severity classification is performed through Kurtosis computation from non-stationary components. Obtained results from real experimentation validate the proposed-method high efficiency, reaching an overall 100% accuracy on detecting and classifying half, one, two BRBs, and healthy condition.

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Section: Effectiveness Validationmentioning

confidence: 89%

Automatic Early Broken-Rotor-Bar Detection and Classification Using Otsu Segmentation

et al. 2020

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“…Broken rotor bar (BRB) fault produces fluctuations and reduces the amplitude of the torque. As a result, they can develop mechanical vibrations and greater fluctuation, which can lead to harmful effects on the machine [4]. Increased number of broken bars creates a high impact on the rotor side during the open‐loop drive system [5–7].…”

Section: Introductionmentioning

confidence: 99%

Broken rotor bar fault detection using Hilbert transform and neural networks applied to direct torque control of induction motor drive

R¹,

Irudayaraj

Saravanan³

et al. 2020

IET power electron.

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“…In contrast, some invasive methods have also been proposed to detect TTFs, as reported in [49–51]. The method in [49] measures the flux crossing each stator tooth, meaning that numerous sensors (as much as the number of stator slots) are needed to measure the air‐gap flux.…”

Section: Introductionmentioning

confidence: 99%

“…The use of flux sensors is not a novel approach to detect the RMF in motors, and their use was introduced in previous research works [12, 51, 57–59] to detect TTFs in stator and rotor of SCIMs. However, the diagnostic approach proposed in this paper uses the distortion of the RMF along the air‐gap circumference to detect STTFs as well as RTTFs in WRIMs, by focusing on RTTF detection.…”

Section: Introductionmentioning

confidence: 99%

Stator and rotor turn‐to‐turn fault detection in wound rotor induction machines based on the air‐gap magnetic field distortion

Nazemi

Haghjoo

Cruz³

et al. 2020

IET Electric Power Applications

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An efficient online flux‐based diagnostic approach is proposed to detect turn‐to‐turn faults in the stator and rotor windings of wound rotor induction machines, at an early stage of development, which can be applied in the online mode. Some flux sensors are installed in the stator slots to detect the rotational magnetic field at various positions along the air‐gap circumference. These sensors measure the flux linked to all winding coil groups, in the three phases. Any fault occurring either in the stator or rotor windings will disturb the air‐gap flux and create an asymmetry in the rotational magnetic field, which is detectable through the difference of the induced voltages in the corresponding flux sensors. The proposed diagnostic technique is evaluated by finite element analysis as well as by multiple experimental tests. The obtained results show that such invasive technique is able to detect and discriminate various faults in the stator and rotor windings with high accuracy, low calculation burden, proper sensitivity and appropriate robustness in the face of machine load level variations.

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Broken Rotor Bar Detection in Induction Motors Based on Air-Gap Rotational Magnetic Field Measurement

Cited by 34 publications

References 29 publications

Automatic Early Broken-Rotor-Bar Detection and Classification Using Otsu Segmentation

Automatic Early Broken-Rotor-Bar Detection and Classification Using Otsu Segmentation

Broken rotor bar fault detection using Hilbert transform and neural networks applied to direct torque control of induction motor drive

Stator and rotor turn‐to‐turn fault detection in wound rotor induction machines based on the air‐gap magnetic field distortion

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