Detection of Bearing Faults in Cage Induction Motors Fed by Frequency Converter using Spectral Analysis of Line Current

Duque, Oscar; Perez, M.Pilar; Morinigo, D.

doi:10.1109/iemdc.2005.195695

Cited by 28 publications

(10 citation statements)

References 13 publications

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“…where f 1 is the main supply frequency, n is an entire number, and f v is the vibration characteristic frequency. f v depends on the type of bearing fault (outer or inner race, balls and train defect), with expressions that are a function of the geometry and composition of the bearing [7]. The fault frequencies given by Equation 1are the result of taking into account the deviations in the main component of the airgap field.…”

Section: Fault Signaturesmentioning

confidence: 99%

“…Mainly, the low energy of the vibrations are associated to the fault, which makes it difficult to distinguish in the current spectrum the frequency components related to the fault that may be buried in the noise [1,5,6]. Besides, for inverter-fed motors, the noise is higher and there are other harmonics present in the spectrum, which complicates even more the detection of the faulty related components [7]. Consistently, in [8], denoising techniques are applied to highlight the faulty components in the current spectrum.…”

Section: Introductionmentioning

confidence: 99%

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Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods

et al. 2019

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Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal.

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Section: Fault Signaturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods

et al. 2019

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“…All these influences complicate the utilization of frequency analysis methods [10]. VSI-fed motor faults have been analyzed and initial results are given in literature [11]- [15] but further investigation is still required [8], [16]. Taking into consideration the aforementioned difficulties of the diagnosis of VSI fed motors, one should take into consideration as many fault signatures as possible to enhance the reliability of fault diagnosis [8].…”

Section: Introductionmentioning

confidence: 99%

Experimental identification of induction motor broken bar characteristic frequencies using a programmable power source

Duque-Pérez

Moriñigo-Sotelo

Rodriguez-Carrascal

et al. 2010

The XIX International Conference on Electrical Machines - ICEM 2010

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 Abstract --The introduction of Voltage Source Inverter fed motors has produced significant changes in the field of diagnostics being necessary further research to overcome various challenges. To enhance the diagnosis of motor faults it is advisable to consider as many fault signatures as possible, so, in this paper it is analyzed the detection of broken bars using the spectra around harmonics introduced by the power supply. Since the harmonic content of the voltage waveform and the amplitude of the harmonics depend on the voltage supply, in order to establish diagnosis criteria that are independent of the voltage supply, an induction motor has been tested being supplied by a Programmable Power Source, which has allowed to completely control the harmonic content of the input voltage waveform. The results are discussed and some guidelines for condition monitoring are suggested.

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“…where p is the number of pair of poles and q depends on the kind of bearing fault and on the characteristics on the bearing (number of balls, ball diameter, and ball pitch diameter, respectively, and the contact angle of the ball with the races) [52]. For mixed eccentricity, in VSI-fed motors, the fault frequencies are observed as sideband pairs around principal current harmonics introduced by the power supply, situated at a distance equal to the mechanical rotation frequency, according to (3)…”

Section: Fault Signaturesmentioning

confidence: 99%

“…As fault signatures, the two first sidebands [n = 1, 2 in (2)] for the fundamental train frequency [52] have been chosen. Figs.…”

Section: B Bearingsmentioning

confidence: 99%

Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts

García-Escudero

Duque-Pérez

Fernandez-Temprano

et al. 2017

IEEE Trans. on Ind. Applicat.

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Abstract-A considerable amount of papers has been published in recent years proposing supervised classifiers to diagnose the health of a machine. The usual procedure with these classifiers is to train them using data acquired through controlled experiments, expecting them to perform well on new data, classifying correctly the condition of a motor. But, obviously, the new motor to be diagnosed cannot be the same that has been used during the training process; it may be a motor with different characteristics and fed from a completely different source. These different conditions between the training process and the testing one can deeply influence the diagnosis. To avoid these drawbacks, in this paper, a new method is proposed, which is based on robust statistical techniques applied in quality control applications. The proposed method is based on the online diagnosis of the operating motor and can detect deviations from the normal operational conditions. A robust approach has been implemented using high-breakdown statistical techniques, which can reliably detect anomalous data that often cause an unexpected overestimation of the data variability, reducing the ability of standard procedures to detect faulty conditions in earlier stages. A case study is presented to prove the validity of the proposed approach. Motors of different characteristics, fed from the power line and several different inverters, are tested. Three different fault conditions are provoked: a broken bar, a faulty bearing, and mixed eccentricity. Experimental results prove that the proposed approach can detect incipient faults.

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Detection of Bearing Faults in Cage Induction Motors Fed by Frequency Converter using Spectral Analysis of Line Current

Cited by 28 publications

References 13 publications

Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods

Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods

Experimental identification of induction motor broken bar characteristic frequencies using a programmable power source

Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts

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