scholarly journals A Novel Semi-Supervised Feature Extraction Method and Its Application in Automotive Assembly Fault Diagnosis Based on Vision Sensor Data

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2545 ◽  
Author(s):  
Xuan Zeng ◽  
Shi-Bin Yin ◽  
Yin Guo ◽  
Jia-Rui Lin ◽  
Ji-Gui Zhu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Supervised Learning ◽  
Extraction Method ◽  
Global Structure ◽  
Experimental Results ◽  
Sensor Data ◽  
Feature Extraction Method ◽  
Fisher Discriminant ◽  
Automotive Assembly

The fault diagnosis of dimensional variation plays an essential role in the production of an automotive body. However, it is difficult to identify faults based on small labeled sample data using traditional supervised learning methods. The present study proposed a novel feature extraction method named, semi-supervised complete kernel Fisher discriminant (SS-CKFDA), and a new fault diagnosis flow for automotive assembly was introduced based on this method. SS-CKFDA is a combination of traditional complete kernel Fisher discriminant (CKFDA) and semi-supervised learning. It adjusts the Fisher criterion with the data global structure extracted from large unlabeled samples. When the number of labeled samples is small, the global structure that exists in the measured data can effectively improve the extraction effects of the projected vector. The experimental results on Tennessee Eastman Process (TEP) data demonstrated that the proposed method can improve diagnostic performance, when compared to other Fisher discriminant algorithms. Finally, the experimental results on the optical coordinate data proves that the method can be applied in the automotive assembly process, and achieve a better performance.

Ear Recognition Based on Supervised Learning Using Gabor Filters

2010 ◽  
Vol 29-32 ◽  
pp. 1127-1132
Author(s):  
Yong Liang Xiao ◽  
Shao Ping Zhu
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Extraction Method ◽  
Nearest Neighbor ◽  
Gabor Filters ◽  
Experimental Results ◽  
Feature Extraction Method ◽  
Nearest Neighbor Classifier ◽  
Ear Recognition ◽  
Neighbor Classifier

In this paper, we introduce a new ear recognition approach including a feature extraction method and the recognition framework. Firstly, we use a supervised kernel neighborhood preserving projection to extract discriminately ear feature, then we use generalized locally nearest neighbor classifier to recognize ear. Experimental results on USTB ear database show the effectiveness of our method.

Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

2021 ◽  
Vol 63 (8) ◽  
pp. 465-471
Author(s):  
Shang Zhiwu ◽  
Yu Yan ◽  
Geng Rui ◽  
Gao Maosheng ◽  
Li Wanxiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Extraction Method ◽  
Dynamic Time Warping ◽  
Spectral Feature ◽  
Time Warping ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Dynamic Time

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

Wear Particles Recognition Based on Improved LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1874-1878
Author(s):  
Guo De Wang ◽  
Zhi Sheng Jing ◽  
Guo Wei Qin ◽  
Shan Chao Tu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Binary Sequence ◽  
Texture Feature ◽  
Experimental Results ◽  
Wear Particles ◽  
Texture Feature Extraction ◽  
Feature Extraction Method

Wear particles recognition is a key link in the process of Ferrography analysis. Different kinds of wear particles vary greatly in texture, texture feature is one of the most important feature in wear particles recognition. Local Binary Pattern (LBP) is an efficient operator for texture description. The binary sequence of traditional LBP operator is obtained by the comparison between the gray value of the neighborhood and the gray value of the center pixel of the neighborhood, the comparison is too simple to cause the loss of the texture. In this paper, an improved LBP operator is presented for texture feature extraction and it is applied to the recognition of severe sliding particles, fatigue spall particles and laminar particles. The experimental results show that our method is an effective feature extraction method and obtains better recognition accuracy compared with other methods.

scholarly journals A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1319
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Yucai Li ◽  
Wei Lin ◽  
Jinjuan Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Vibration Signal ◽  
Practical Significance ◽  
Stable Operation ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Multi Scale

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

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