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

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Guoqing An ◽  
Hongru Li
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Piezoelectric Ceramics ◽  
Ultrasonic Motor ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
State Identification ◽  
Fault Feature Extraction ◽  
Degradation State Identification ◽  
Degradation State

The cracking of piezoelectric ceramics is the main reason of failure of an ultrasonic motor. Since the fault information is too weak to reflect the condition of piezoelectric ceramics especially in the early degradation stage, a fault feature extraction method based on multiscale morphological spectrum and permutation entropy is proposed. Firstly, a signal retaining the morphological feature under different scales is reconstructed with multiscale morphological spectrum components. Then, the permutation entropy of the reconstructed signal is taken as the fault feature of piezoelectric ceramics. Furthermore, a sensitivity factor is defined to optimize the embedded dimension and delay time of permutation entropy according to double sample Z value analysis. Finally, a matrix composed of the probability distributions, obtained from permutation entropy calculation, is applied for the degradation state identification by means of probability distribution divergence. The analysis of actual test data demonstrates that this method is feasible and effective.

scholarly journals Fault Feature Extraction and Degradation State Identification for Piezoelectric Ceramics Cracking in Ultrasonic Motor Based on Multi-Scale Morphological Gradient

2019 ◽  
Vol 24 (4) ◽  
pp. 749-763
Author(s):  
Guoqing An ◽  
Hongru Li ◽  
Baiyan Chen
Keyword(s):  
Feature Extraction ◽  
Piezoelectric Ceramics ◽  
Ultrasonic Motor ◽  
Scale Morphology ◽  
Multi Scale ◽  
Morphological Gradient ◽  
State Identification ◽  
Fault Feature Extraction ◽  
Degradation State Identification ◽  
Degradation State

Piezoelectric ceramics cracking is one of the main faults of the ultrasonic motor. According to the morphological mathematics and information entropy, a method based on multi-scale morphological gradient was proposed for ceramics fault feature extraction and degradation state identification. To solve the problem that traditional multi-scale morphology spectral (MMS) entropy cannot exactly describe the performance degradation of the piezoelectric ceramics, multi-scale morphology gradient difference (MMGD) entropy was proposed to improve the sensitivity to the fault. Furthermore, multi-scale morphology gradient singular (MMGS) entropy was presented to reduce the system noise interference to the useful fault information. The disturbance analysis of temperature, load, and noise for MMGD entropy and MMGS entropy was also given in this paper. Combining the advantages of the above two entropies, a standard degradation mode matrix was built to distinguish the degradation state via the grey correlation analysis. The analysis of actual test samples demonstrated that this method is feasible and effective to extract the fault feature and indicate the degradation of piezoelectric cracking in ultrasonic motor.

An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

2019 ◽  
Vol 26 (3-4) ◽  
pp. 146-160
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yongbo Li ◽  
Xiaoqiang Du
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Extraction Method ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Multichannel Signals ◽  
Fault Feature Extraction ◽  
Fault Information

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

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

2019 ◽  
Vol 21 (6) ◽  
pp. 1651-1664 ◽  
Author(s):  
Hongru Li ◽  
Guoqing An ◽  
Kaiyao Song ◽  
Rui Li ◽  
Huiqin Sun
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Piezoelectric Ceramic ◽  
Ultrasonic Motor ◽  
Cross Entropy ◽  
Feature Extraction Method

Fault Feature Extraction Method of Rotating Machinery Based on DeepLabV3+ Semantic Segmentation

2021 ◽  
Author(s):  
Kai Chen ◽  
Minxiang Wei ◽  
Xinda Chen ◽  
JiaWei Yang ◽  
Yuhang Pei ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Semantic Segmentation ◽  
Rotating Machinery ◽  
Feature Extraction Method ◽  
Fault Feature Extraction
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