Rotor failure detection of induction motors by wavelet transform and Fourier transform in non-stationary condition

Costa, Cesar da; Kashiwagi, Masamori; Mathias, Mauro Hugo

doi:10.1016/j.csmssp.2015.05.001

Cited by 54 publications

(48 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The FFT is useful in finding failures in the static signals due to its functioning in the frequency domain only. Also, the FFT-based Motor Current Signature Analysis relies on sideband components around elemental harmonic fault detection where the amplitude of side-band components varies along with load, and therefore, it is more apt to detect fault under balanced load but not in varying loads [15].…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

Incipient Fault Detection of the Inverter Fed Induction Motor Drive

Ramana¹,

Baskar²

2017

IJPEDS

View full text Add to dashboard Cite

Inverter fed Induction motor drives are deployed across a variety of industrial and commercial applications. Although the drives in the question are well known for their reliable operation in any type of environment, it becomes an important daunting critical task to have them in continuous operation as per the applications" requirement. Identifying the faulty behavior of power electronic circuits which could lead to catastrophic failures is an attractive proposition. The cost associated with building systems devoted for monitoring and diagnosis is high, however such cost could be justified for the safety-critical systems. Commonly practiced methods for improving the reliability of the power electronic systems are: designing the power circuit conservatively or having parallel redundant operation of components or circuits and clearly these two methods are expensive. An alternative to redundancy is fault tolerant control, which involves drive control algorithm, that in the event of fault occurrence, allows the drive to run in a degraded mode. Such algorithms involve on-line processing of the signals and this requires Digital Signal Processing of the signals. This paper presents the FFT and Wavelet transform techniques for on-line monitoring and analyzing the signals such as stator currents.

show abstract

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

Incipient Fault Detection of the Inverter Fed Induction Motor Drive

Ramana¹,

Baskar²

2017

IJPEDS

View full text Add to dashboard Cite

show abstract

“…However, when the EEMD method is applied independently for decomposing signals and reducing noise, the information in the high-frequency components is also lost as some IMFs are discarded [14]. The WT method has a good performance on the suppression of random noise by having the properties of multi-scale, low entropy, and decorrelation [15]. Jumah et al [16] proposed a method using wavelet transform and various thresholding techniques, which has a good effect on removing one-dimensional Gaussian white noise.…”

mentioning

confidence: 99%

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Niu

et al. 2020

Entropy

View full text Add to dashboard Cite

Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.Entropy 2020, 22, 290 2 of 28 functions (IMFs) by EMD [10]. Aiming at the shortcomings of the EMD, such as modal mixture and end effect, which affect the accuracy of signal decomposition [11], Wu et al. [12] improved the EMD method with auxiliary noise and proposed the ensemble empirical mode decomposition (EEMD) method.The EEMD method results in signal decomposition with anti-noise characteristics, reduces reconstruction errors, and improves the quality of IMFs by adding Gaussian white noise to the original signal. Therefore, the EEMD method is widely used in signal processing and fault diagnosis [13]. However, when the EEMD method is applied independently for decomposing signals and reducing noise, the information in the high-frequency components is also lost as some IMFs are discarded [14]. The WT method has a good performance on the suppression of random noise by having the properties of multi-scale, low entropy, and decorrelation [15]. Jumah et al. [16] proposed a method using wavelet transform and various thresholding techniques, which has a good effect on removing one-dimensional Gaussian white noise. However, there is no uniform standard for the operation of the wavelet threshold method, which has a greater impact on the final result. The wavelet hard threshold denoising method generates discontinuous points and loses some vital information [17]. The wavelet soft threshold denoising method causes distortion phenomena, such as edge blur effect [18].Although the wavelet threshold denoising method effectively removes noise from high-frequency signals, the effect is not ideal, and the useful signals still mix with noise signals. Nevertheless, the wavelet semi-soft threshold with the advantages of the hard and soft threshold can not only preserve the integrity of the signal but al...

show abstract

“…Spectrum analysis is a method for identifying the frequencies of a signal. Fourier transform deals with signals that are studied respect to the time parameter as sine and cosines; and to the frequency parameter that is the same signal which is categorized according the frequencies [7].…”

Section: Introductionmentioning

confidence: 99%

A Comparisonal Study on Unbalance Failure in the Perspective of Vibration and Electrical Consumption Analysis

Erol

2016

International Journal of Applied Mathematics, Electronics and Computers

View full text Add to dashboard Cite

Abstract:In this research, condition of resonance effect which is a common problem for mechanical constructions has been studied in perspective of unbalance failure of a bearing. A test setup designed respect to case practices; constructed and located in laboratory conditions. A bearing in the electrical motor, that is one of the elements in the test setup, has been selected for gathering data in vibration and electrical consumption during the test. The purpose of the research is testing condition of unbalance failure and resonance for studying comparison in various predictive maintenance approaches. Test has been implemented under the electricity frequency of 40.5 Hz that induced the electrical motor for determining rotational speed. According to the analysis results, inspecting of unbalance failure and resonance problem has been detected more clearly by vibration analysis.

show abstract

Rotor failure detection of induction motors by wavelet transform and Fourier transform in non-stationary condition

Cited by 54 publications

References 17 publications

Incipient Fault Detection of the Inverter Fed Induction Motor Drive

Incipient Fault Detection of the Inverter Fed Induction Motor Drive

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

A Comparisonal Study on Unbalance Failure in the Perspective of Vibration and Electrical Consumption Analysis

Contact Info

Product

Resources

About