Distinct Fault Analysis of Induction Motor Bearing Using Frequency Spectrum Determination and Support Vector Machine

Pandarakone, Shrinathan Esakimuthu; Mizuno, Yukio; Nakamura, Hisahide

doi:10.1109/tia.2016.2639453

Cited by 82 publications

(30 citation statements)

References 28 publications

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“…Support vector machine (SVM) is based on the VC dimension theory of statistical learning theory and the structural risk minimization principle, which has the advantage of solving the classification problem of high dimensional, small sample and nonlinear data. It has good generalization ability [25]. SVM obtains global optimal solution by optimization method, which can prevent over-learning and local minimum problems of traditional statistical methods.…”

Section: Svm Recognition Classificationmentioning

confidence: 99%

Research on power equipment recognition method based on image processing

Wang

Meng

2019

J Image Video Proc.

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Electric energy is an indispensable energy in life, and the power network is the basis to ensure its normal circulation, in which the operation status of power equipment is one of the key factors to determine the safe and stable operation of the power network. In the information age, the traditional manual periodic inspection and the existing method of relying on manual monitoring equipment operation status can no longer meet the needs of safe operation of equipment; relying on computer technology and image recognition technology to achieve automatic identification of power equipment has become a research hot spot. In order to realize automatic identification of power equipment, this paper presents a method of recognition of power equipment based on image processing. Firstly, the power equipment image is preprocessed by various denoising and sharpening algorithms to remove the noise and distortion of the image and improve the image quality; secondly, the SIFT algorithm is used to extract image features, and PCA algorithm is used to reduce the dimension; finally, the support vector machine is used to classify and recognize the image. The simulation results show that the proposed denoising and sharpening algorithms can process images well and improve the quality of images. The support vector machine is used to classify the image features processed by SIFT algorithm and PCA algorithm, and the automatic recognition of power equipment is realized. And the method of power identification based on image processing proposed in this paper has good recognition accuracy.

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Section: Svm Recognition Classificationmentioning

confidence: 99%

Research on power equipment recognition method based on image processing

Wang

Meng

2019

J Image Video Proc.

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“…By taking the windowed Fourier transform of ( ) and < −1> ( ), the following equations are obtained: Substituting 17- (18) into (14), the nd covariation spectrum can be written aŝ…”

Section: Fractional Lower Order Welch Covariation Spectrummentioning

confidence: 99%

“…A new detection method of the change in amplitude was presented based on principal component analysis, which was used to detect the rolling element bearing fault [13]. Pandarakone and the others proposed a distinct motor bearing analysis method employing support vector machine for frequency spectrum 2 Mathematical Problems in Engineering determination [14], although the existing frequency spectrum methods have been well applied, which cannot work in peaked noise environment.…”

Section: Introductionmentioning

confidence: 99%

Applications of Fractional Lower Order Frequency Spectrum Technologies to Bearing Fault Analysis

Long

Wang

2019

Mathematical Problems in Engineering

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The traditional spectral analysis method is used to study the characteristics of bearing fault signals in frequency domain, which is reasonable and effective in general cases. However, it is proved that the fault signals have heavy tails in this paper, which are α stable distribution, and 1<α<2, and even the noises belong to α stable distribution. Then the conventional spectral analysis methods degenerate and even fail under α stable distribution environment. Several improved frequency spectral analysis methods are proposed employing fractional lower order covariation or fractional lower order covariance in this paper, including fractional lower order Blackman-Tukey covariation spectrum (FLOBTCS), fractional lower order periodogram covariation spectrum (FLOPCS), and fractional lower order welch covariation spectrum (FLOWCS). In order to suppress side lobe and improve resolution, we present novel fractional lower order autoregression (FLO-AR) and fractional lower order autoregressive moving average (FLO-ARMA) parameter model frequency spectrum methods, and the calculation steps are summarized. The proposed spectrum methods are compared with the existing methods based on second-order statistics under Gaussian and SαS distribution environments, and the results show that the new algorithms have better performance than the traditional methods. Finally, the improved methods are applied to estimate frequency spectrums of the normal and outer race fault signals, and it is demonstrated that they are effective for fault diagnosis.

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“…In industrial applications, Fourier analysis is probably the most popular analysis technique. Thus, the fast Fourier transform (FFT) is usually applied to obtain the frequency spectrum [14]. The Extended Park's Vector Approach (EPVA) uses the frequency spectrum of the Park's vector module to improve the SNR of the current signals by converting the fundamental component to the DC component, which successfully detects different types of bearing faults [15].…”

Section: Introductionmentioning

confidence: 99%