A quality abnormality diagnosis method for dynamic process based on pattern recognition

2014 ◽  
Author(s):  
Meijing Tian ◽  
Huiqin Jiang ◽  
Ren Xu ◽  
Yumin Liu
Keyword(s):  
Pattern Recognition ◽  
Dynamic Process ◽  
Diagnosis Method

A MSVM Quality Pattern Recognition Model for Dynamic Process

2013 ◽  
Vol 433-435 ◽  
pp. 555-561
Author(s):  
Yu Min Liu ◽  
Hao Fei Zhou ◽  
Shuai Zhang
Keyword(s):  
Pattern Recognition ◽  
Dynamic Process ◽  
Simulation Experiment ◽  
Recognition Accuracy ◽  
Kernel Functions ◽  
Automatic Production ◽  
Recognition Model ◽  
Online Quality Control ◽  
Recognition Quality ◽  
Abnormal Pattern

Quality abnormal pattern recognition for dynamic process is the key problem to achieve the online quality control and diagnose of automatic production. Firstly, this paper analyzed the quality patterns of dynamic process. Secondly, we established recognition model of quality recognition in dynamic process using MSVM and compared the SVM recognition accuracy of different kernel functions for different quality patterns. Simulation experiment indicates that different SVM classifiers should choose specified kernel functions to recognition quality patterns. At last, we established MSVM recognition model of quality pattern in dynamic process using multi-kernel function according to the experiment results.

scholarly journals Intelligent Fault Diagnosis of Wind Turbine Gearbox based on Refined Generalized Multi-scale State Joint Entropy and RSFS Feature Selection

2021 ◽  
Author(s):  
Wei Dong ◽  
shuqing zhang ◽  
Mengfei Hu ◽  
Liguo Zhang ◽  
Haitao Liu
Keyword(s):  
Pattern Recognition ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Feature Reduction ◽  
Maintenance Cost ◽  
Joint Entropy ◽  
Wind Turbine Gearbox ◽  
Multi Scale ◽  
Diagnosis Method

Abstract The fault diagnosis of gearbox and bearing in wind turbine is crucial to improve service life and reduce maintenance cost. This paper proposes a novel fault diagnosis method based on refined generalized composite multi-scale state joint entropy (RGCMSJE), robust spectral learning framework for unsupervised feature selection (RSFS) and extreme learning machine (ELM) to identify the different health conditions of gearboxes, including feature extraction, feature reduction and pattern recognition. In this method, MAED is firstly adopted to assist RGCMSJE in parameter selection. Second, RGCMSJE is utilized to extract the multi-scale features of gearbox vibration signal and construct high-dimension feature set. Thirdly, RSFS method is used to reduce the dimension of high-dimensional RGCMSJE feature set. In the end, the obtained low-dimensional features are input to the ELM classifier to realize fault pattern recognition. Through two gearbox fault diagnosis experiments, the effectiveness of the fault diagnosis method is verified. The analysis results show that this method can effectively and accurately identify different fault types of wind turbine gearbox.

scholarly journals A Research on Automation Diagonsis of Pattern Recognition in Stained HEP-2 Cells

2019 ◽  
Vol 8 (11S2) ◽  
pp. 337-345
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Medical Image ◽  
Medical Image Processing ◽  
High Variability ◽  
Image Processing Techniques ◽  
Advantages And Disadvantages ◽  
Initial Work ◽  
Diagnosis Method ◽  
Processing Techniques

Diagnosis of autoimmune diseases can be achieved via Indirect Immunofluorescence (IIF) images using human epithelial (HEp-2) cell as substrate in laboratory. The automation of this diagnosis method is still challenging because of using various liquids to fix the HEp-2 cells in the slides. Due to various fixation methods, nuclear morphology of cell suffers high variability. This survey reviews all the difficulties in the analysis and recognition of pattern recognition and surveys various image processing techniques which leads to the automation diagnosis. This work consist of advantages and disadvantages of various procedures. Eventually, comparison of their corresponding results are presented. I assure that this initial work may attract many medical image processing researchers to enter into this field.

Fault diagnosis based on grey relational analysis and synergetic pattern recognition for aero-engine gas-path systems

2019 ◽  
Vol 233 (12) ◽  
pp. 4598-4605
Author(s):  
Bangcheng Zhang ◽  
Jing Chen ◽  
Xiaojing Yin ◽  
Zhi Gao
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Grey Relational Analysis ◽  
Relational Analysis ◽  
Aero Engine ◽  
Diagnosis Method ◽  
Path System ◽  
Aero Engines ◽  
The Many ◽  
Grey Relational

The gas-path system is an important sub-system in aero-engines. There are various indistinguishable faults in aero-engine gas-path systems. These faults are easily misjudged because the characteristic parameters are similar. Due to the many kinds of faults, current studies have poor accuracy in distinguishing similar faults. To improve fault diagnosis accuracy for gas-path systems, a fault diagnosis method based on grey relational analysis and synergetic pattern recognition is proposed. In the proposed method, grey relational analysis is used to initially distinguish the faults into different types and obtain similar fault types. Synergetic pattern recognition contributes to accurately diagnose faults which are difficult to recognize. A case study is used to verify the effectiveness and accuracy of the proposed model. The results show that faults in common types of gas-path systems can be diagnosed accurately by the proposed method.

Dynamic Process of Quality Abnormal Pattern Recognition Based on PCA-SVM

2013 ◽  
Vol 860-863 ◽  
pp. 2686-2689
Author(s):  
Yu Min Liu ◽  
Shuai Zhang
Keyword(s):  
Pattern Recognition ◽  
Dynamic Process ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Svm Classifier ◽  
Feature Selection Technique ◽  
Automatic Production ◽  
Practical Applications ◽  
Online Quality Control ◽  
Abnormal Pattern

Quality abnormal pattern recognition for dynamic process is the key problem to achieve the online quality control and diagnose of automatic production. In the practical applications, there are some existing problems such as computational complexity and low recognition accuracy. A recognition method for quality abnormal pattern of dynamic process with PCA-SVM was proposed. This paper proposes a feature selection technique that employs a principal component analysis, to avoid this information loss. Then, the extracted features were treated as input vector for SVM classifier, following a particle swarm optimization algorithm is proposed to improve the generalization performance of the recognizer. Simulation results show that the proposed algorithm has very high recognition accuracy and high generalization ability. It is significant for quality monitoring and diagnosis in manufacture dynamic process.

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