Degradation State Identification of Cracked Ultrasonic Motor by Means of Fault Feature Extraction Method

An, Guoqing; Li, Hongru

doi:10.1155/2019/5180590

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…e indicator-based method is to compare the measured value with the indicator threshold, which is a widely used fault diagnosis method. An and Li [24] proposed the permutation entropy of the reconstructed signal as the fault feature of piezoelectric ceramics. Jafari et al [25] established four different indices and used the well-established Otsu thresholding technique to set the index thresholds for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Failure Probability Modeling of Miniature DC Motors and Its Application in Fault Diagnosis

Xie

Zhao

Zheng

et al. 2021

Shock and Vibration

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This paper proposes a fault diagnosis method for miniature DC motors (MDCMs) in the presence of the uncertainties caused by material and random factors of the production process. In this method, the probability models of fault multiple features are established based on the advantage criterion of the maximum overall average membership to determine the distribution of fault multiple features. The fault diagnosis algorithm is synthesized to obtain the threshold ranges of fault multiple features according to different confidence levels. Experimental test results are presented and analyzed to validate the efficiency and performance of the proposed fault diagnosis method.

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Section: Introductionmentioning

confidence: 99%

Failure Probability Modeling of Miniature DC Motors and Its Application in Fault Diagnosis

Xie

Zhao

Zheng

et al. 2021

Shock and Vibration

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show abstract

“…As far as degradation feature extraction for piezoelectric ceramic is concerned, the energy transmission would be subjected to a disturbance as the traveling wave passes through the crack on ceramic. MEV can reflect the vibration of piezoelectric ceramic in ultrasonic motor [23]. For the reason that DCT has the property of energy aggregation, DCT coefficients of MEV signal will be sensitive to energy change caused by the crack.…”

Section: Introductionmentioning

confidence: 99%

Degradation feature extraction method for piezoelectric ceramic of ultrasonic motor based on DCT-SV cross entropy

An¹,

Song²,

Li³

et al. 2019

J. vibroeng.

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Crack on piezoelectric ceramic is the main reason leading to failure of ultrasonic motors. A novel degradation feature extraction method based on discrete cosine transform (DCT)-singular value (SV) cross entropy was proposed in this paper. In order to improve the correlation with the crack, the DCT coefficients with the property of energy aggregation, were used to extract fault information. To avoid the influence of human factors in traditional DCT de-noising method, a matrix composed of DCT coefficients was constructed, and the SV cross entropy of the matrix was taken as the degradation feature for ultrasonic motor. A numerical simulated noise was added to the measured signal to verify the anti-noise performance of the feature. Analysis of the experimental results demonstrates that the proposed DCT-SV cross entropy is feasible and effective in indicating the degradation of piezoelectric ceramic in ultrasonic motor.

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Degradation State Identification Method for Piezoelectric Ceramic of Ultrasonic Motor Based on Segmented Fractal Dimension and Sparse Representation

Song

et al. 2021

Journal of Testing and Evaluation

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Crack on piezoelectric ceramic is the main reason leading to the degradation of ultrasonic motors. Monitor electrode voltage generated by the positive piezoelectric effect is usually used to monitor the vibration of stator. However, the complexity of monitor electrode voltage signal changes slightly until failure state because of the bonding layer between piezoelectric ceramic and metal elastomer, and it brings difficulties for the degradation state identification. In order to improve the accuracy rate, a method based on segmented fractal dimension and sparse representation was presented in this article. Firstly, segmented fractal dimension was proposed to extract the complexity information of local signal, and the ones of standard samples were taken as the atoms to construct a dictionary. Then, sparse representation of the test sample was calculated according to the constructed dictionary, and the specific steps for the solution were also detailed. Lastly, the test sample’s deviation vectors corresponding to different degradation states were obtained, and the modulus of the vectors were employed to identify the degradation state. The experimental results show that this method is feasible and effective for the degradation state identification of piezoelectric ceramic. It is meaningful for the condition-based maintenance of ultrasonic motors.

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Degradation State Identification of Cracked Ultrasonic Motor by Means of Fault Feature Extraction Method

Cited by 4 publications

References 27 publications

Failure Probability Modeling of Miniature DC Motors and Its Application in Fault Diagnosis

Failure Probability Modeling of Miniature DC Motors and Its Application in Fault Diagnosis

Degradation feature extraction method for piezoelectric ceramic of ultrasonic motor based on DCT-SV cross entropy

Degradation State Identification Method for Piezoelectric Ceramic of Ultrasonic Motor Based on Segmented Fractal Dimension and Sparse Representation

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