Advanced Rotor Fault Diagnosis for Medium-Voltage Induction Motors Via Continuous Transforms

Antonino-Daviu, José A.; Pons-Llinares, J.; Lee, Sang Bin

doi:10.1109/tia.2016.2582720

Cited by 29 publications

(14 citation statements)

References 21 publications

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“…Therefore, f LSH will follow a very characteristic evolution during the starting that resembles a V-pattern. The detection and quantification of this evolution, which has been well characterized in many previous works [8,10], is a reliable evidence of the presence of the harmonic and therefore of the existence of rotor damage.…”

Section: Foundations Of the Transient Analysis Of Electrical Quantitiesmentioning

confidence: 75%

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Electrical Monitoring under Transient Conditions: A New Paradigm in Electric Motors Predictive Maintenance

Antonino-Daviu

2020

Applied Sciences

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Electric motors condition monitoring is a field of paramount importance for industry. In recent decades, there has been a continuous effort to investigate new techniques and methods that are able to determine the health of these machines with high accuracy and reliability. Classical methods based on the analysis of diverse machine quantities under stationary conditions are being replaced by modern methodologies that are adapted to any operation regime of the machine (including transients). These new methods (especially those based on motor startup signal monitoring), which imply the use of advanced signal processing tools, have shown great potential and have provided spectacular advantages versus conventional approaches enabling, among other facts, a much more reliable determination of the machine health. This paper reviews the background of this recent condition monitoring trend and shows the advantages of this new approach with regard to its application to the analysis of electrical quantities. Examples referred to its application to real motors operating in industry are included, proving the huge potential of the transient-based approach and its benefits versus conventional methods.

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Section: Foundations Of the Transient Analysis Of Electrical Quantitiesmentioning

confidence: 75%

“…. ) may lead to false indications (positive or negative) when MCSA is applied to the detection of rotor asymmetries [8,9] and other faults. This situation is illustrated in Table 1 that shows how there are cases in which this consolidated technique may yield erroneous diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Electrical Monitoring under Transient Conditions: A New Paradigm in Electric Motors Predictive Maintenance

Antonino-Daviu

2020

Applied Sciences

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“…The following frequency bands or regions were Once these time-frequency maps are split, several statistical and non-statistical parameters of each of these regions are obtained in order to characterize them. The following ten parameters are considered: (1) signal energy, (2) standard deviation, (3) statistical mean, (4) statistical median, (5) kurtosis, (6) skewness, (7) root mean square, (8) peak-to-average ratio, (9) shape factor, and (10) crest factor. These parameters are chosen because of their ability to provide relevant information about changes and trends in signals by investing a very low cost in computational burden.…”

Section: Single-board Computer-based Processing Unitmentioning

confidence: 99%

“…Each of these techniques is subjected to its own advantages and disadvantages. In this regard, the motor current signature analysis (MCSA) approach has become an extensively used method over the last decades, as several faults can be diagnosed with this approach [4], with frequency-domain procedures being the most preferable ones, as they can deliver results with high reliability [5,6]. However, this approach has some well-known drawbacks when analyzing faults in certain circumstances; for instance, they are not immune to false indications caused by the presence of load torque oscillations or supply voltage fluctuations, because specific spectral components that are non-related to electromechanical faults may appear at same locations as those related to electromechanical faults [7].…”

Section: Introductionmentioning

confidence: 99%

Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis

Zamudio-Ramírez

Osornio-Ríos

Antonino-Daviu

et al. 2020

Sensors

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Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the possibility of combining the information obtained from the analysis of different magnitudes should be explored, in order to achieve more reliable diagnostic conclusions, before the fault can develop into an irreversible damage. This paper proposes a smart-sensor that explores the weighted analysis of the axial, radial, and combination of both stray fluxes captured by a low-cost, easy setup, non-invasive, and compact triaxial stray flux sensor during the start-up transient through the short time Fourier transform (STFT) and characterizes specific patterns appearing on them using statistical parameters that feed a feature reduction linear discriminant analysis (LDA) and then a feed-forward neural network (FFNN) for classification purposes, opening the possibility of offering an on-site automatic fault diagnosis scheme. The obtained results show that the proposed smart-sensor is efficient for monitoring and diagnosing early induction motor electromechanical faults. This is validated with a laboratory induction motor test bench for individual and combined broken rotor bars and misalignment faults.

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“…Furthermore, the high-resolution spectrum techniques such as the estimation of signal parameters via rotational invariance techniques (ESPRITs) [23,24], multiple signal classification (MUSIC) [25,26], root-MUSIC [25], and zoom-MUSIC (ZMUSIC) [26] are investigated in rotor faults detection. Finally, the time-frequency analysis methods of short time Fourier transform (STFT) [27,28], wavelet [29][30][31], and Wigner-Ville transform [32] are also applied to the MCSA.…”

Section: Introductionmentioning

confidence: 99%

Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection

et al. 2019

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Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs.

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Advanced Rotor Fault Diagnosis for Medium-Voltage Induction Motors Via Continuous Transforms

Cited by 29 publications

References 21 publications

Electrical Monitoring under Transient Conditions: A New Paradigm in Electric Motors Predictive Maintenance

Electrical Monitoring under Transient Conditions: A New Paradigm in Electric Motors Predictive Maintenance

Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis

Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection

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