Quantitative analysis of noninvasive diagnostic procedures for induction motor drives

Eltabach, Mario; Antoni, Jérôme; Najjar, Maan E. El

doi:10.1016/j.ymssp.2007.03.006

Cited by 19 publications

(4 citation statements)

References 16 publications

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“…It must be considered, when capturing the stator current signal, that the sampling frequency sample plays an important role. Generally, statistical bands of fault-indicative frequencies can be ascertained from theory analysis and many MCSA experiments [2,8,[21][22][23]. Table 1 summarizes the range of sideband frequencies in terms of faulty types.…”

Section: Methods Of Antialiasing Signal Acquisition When Carry-mentioning

confidence: 99%

Practical Aspects of Broken Rotor Bars Detection in PWM Voltage-Source-Inverter-Fed Squirrel-Cage Induction Motors

Zhu

Gao

et al. 2013

Journal of Applied Mathematics

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Broken rotor bars fault detection in inverter-fed squirrel cage induction motors is still as difficult as the dynamics introduced by the control system or the dynamically changing excitation (stator) frequency. This paper introduces a novel fault diagnosis techniques using motor current signature analysis (MCSA) to solve the problems. Switching function concept and frequency modulation theory are firstly used to model fault current signal. The competency of the amplitude of the sideband components at frequencies (1±2s)fsas indices for broken bars recognition is subsequently studied in the controlled motor via open-loop constant voltage/frequency control method. The proposed techniques are composed of five modules of anti-aliasing signal acquisition, optimal-slip-estimation based on torque-speed characteristic curve of squirrel cage motor with different load types, fault characteristic frequency determination, nonparametric spectrum estimation, and fault identification for achieving MCSA efficiently. Experimental and simulation results obtained on 3 kW three-phase squirrel-cage induction motors show that the model and the proposed techniques are effective and accurate.

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Section: Methods Of Antialiasing Signal Acquisition When Carry-mentioning

confidence: 99%

Practical Aspects of Broken Rotor Bars Detection in PWM Voltage-Source-Inverter-Fed Squirrel-Cage Induction Motors

Zhu

Gao

et al. 2013

Journal of Applied Mathematics

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“…Through most relevant literature, three major classes of methods are discovered which support the task of fault monitoring [8]. The first class is based on a signal analysis [12,13] that uses spectra in a frequency domain, time domain, time-frequency domain, and high-order harmonics. The second approach is based on analytical modeling [14] that involves mathematical models to measure input and output feature such as residuals, state estimation, and parameter estimation that incorporate the artificial intelligence (AI) to online automate analyze the health of induction motor through measured signals [15].…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Methods for Condition Monitoring and Electrical Fault Diagnosis of Induction Motors

Sheikh¹,

Nor²,

Ibrahim³

et al. 2017

Fault Diagnosis and Detection

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This chapter provides a comprehensive analysis of noninvasive methods to diagnose stator winding insulation faults of an induction motor. Further, a novel noninvasive method is proposed to diagnose the root cause of winding failure due to unbalanced voltage to avoid catastrophic failure. Therefore, a winding function approach is utilized to derive an analytical expression for stator winding distribution and magnetomotive force (MMF). This tactic qualifies the conductor segment that generates MMF, and it also helps to analyze a healthy current spectrum. One can easily observe higher order harmonics in current spectrum; therefore, a new series of rotor harmonics is introduced to diagnose unbalanced supply. The locus of these harmonics is dependent on the poles, rotor bars, and slip. Due to the rapid complexity in industrial plants, it is inconceivable to continue human inspection to diagnose the faults. Thus, to avoid human inspection, in addition to new series of rotor harmonic, a fully automatic method based on neural network is proposed. This method not only diagnoses unbalanced voltage but it also recognize the percentage of unbalanced voltage by use of feed-forward multilayer perceptron (MLP) trained by back propagation. Finally, the experimental results shows the validation of this research work proposed method.

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“…Based on the electrical modeling of the machine [13,14] and on the choice of the sensor-type, several approaches have been proposed [15][16][17][18][19]. Usually, but not only, the major manner of acquiring a significant signal from the motor is made by acoustic [20], vibratory [21,22] or current acquisition [23,24]. Each has its advantages and of course its drawbacks; it will depend on the default characterization and eventually the motor drive particularity [21,[25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…Owing to its non-invasive or non-intrusive capability [24], stator current acquisition is increasingly put in place. For squirrel-cage rotor induction motors, electrical defects such as rotor bar defects could be seen inside the stator current signal by monitoring the frequencies around the supply frequency [1,7,28].…”

Section: Introductionmentioning

confidence: 99%

Oblique projection pre-processing and TLS application for diagnosing rotor bar defects by improving power spectrum estimation

Bouleux

2013

Mechanical Systems and Signal Processing

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Diagnosing defects on rotating machines can be reached by several angles. When dealing with asynchronous motor drive, such physical elements rotate that a natural angle for treating the healthiness of the motor can be obtained by the use of spectral analysis tools. It is now stated that electrical or mechanical defects, which appear periodically as well, can be retrieved by analyzing the amplitude of particular frequencies inside an estimated power spectrum. When dealing with broken rotor bars detection it is essential to accurately localize the frequencies related to the slip inside the power spectrum. The diagnosis is thereafter made by indicators given with respect to their power. For actual low level of load operations, the supply frequency generally masks the frequencies which could be exploited for indicators. Therefore, we propose to cancel, as well as possible, the contribution of this supply frequency to develop the useful and closely components. The resolution should be very thin for the components to be estimated. In consequence, we use a prior-knowledge subspace-based frequency estimator, already developed in the literature, we complete with an Oblique Projection coupled with a Total Least Squares solution for estimating the power of the resulting estimated frequencies. Finally, we show by means of a real application how it contributes to improve the power spectrum estimation when compared to the FFT or periodogram-based analysis and how the aforementioned power spectrum makes the diagnosis indicator of rotor bars efficient.

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Quantitative analysis of noninvasive diagnostic procedures for induction motor drives

Cited by 19 publications

References 16 publications

Practical Aspects of Broken Rotor Bars Detection in PWM Voltage-Source-Inverter-Fed Squirrel-Cage Induction Motors

Practical Aspects of Broken Rotor Bars Detection in PWM Voltage-Source-Inverter-Fed Squirrel-Cage Induction Motors

Noninvasive Methods for Condition Monitoring and Electrical Fault Diagnosis of Induction Motors

Oblique projection pre-processing and TLS application for diagnosing rotor bar defects by improving power spectrum estimation

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