scholarly journals Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 822
Author(s):  
Jiaxin Zhang ◽  
Wenjia Luo ◽  
Yiyang Dai
Keyword(s):  
Fault Diagnosis ◽  
Principal Component ◽  
Gas Absorption ◽  
Kernel Principal Component Analysis ◽  
Sensor Faults ◽  
Tennessee Eastman Process ◽  
Acid Gas ◽  
Diagnosis Method ◽  
Diagnostic Framework ◽  
Speed And Accuracy

This study considers the problem of distinguishing between process and sensor faults in nonlinear chemical processes. An integrated fault diagnosis framework is proposed to distinguish chemical process sensor faults from process faults. The key idea of the framework is to embed the cycle temporal algorithm into the dynamic kernel principal component analysis to improve the fault detection speed and accuracy. It is combined with the fault diagnosis method based on the reconstruction-based contribution graph to diagnose the fault variables and then distinguish the two fault types according to their characteristics. Finally, the integrated fault diagnosis framework is applied to the Tennessee Eastman process and acid gas absorption process, and its effectiveness is proved.

Fault Diagnosis Method for the Train Axle Box Bearing Based on KPCA and GA-SVM

2013 ◽  
Vol 441 ◽  
pp. 376-379 ◽  
Author(s):  
Ci Wang ◽  
Li Min Jia ◽  
Xiao Feng Li
Keyword(s):  
Fault Diagnosis ◽  
Critical Role ◽  
Principal Component ◽  
Nonlinear Process ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Ring Fault ◽  
Rolling Element ◽  
Diagnosis Method ◽  
Train Safety

Online fault diagnosis for the train axle box bearings is a wide and important study topic since it plays a critical role in train safety. Due to the vibration signals nonlinear and non-stationary characteristics, accuracies of the methods such as neural network and hierarchical clustering are less than 90% which are not satisfying. In this paper, kernel principal component analysis (KPCA), a nonlinear process technique, was to tackle each signals 18 feature parameters for extracting the main features to reflect the signal characteristics. Then, in fault pattern recognition, support vector machine (SVM) based on genetic algorithm (GA) was used to identify the current fault type of the bearings, including normal, outer ring fault, inner ring fault and rolling element fault. The results show that the prediction accuracy of GA-SVM method reaches to 96.33%, which is quite effective.

scholarly journals Sensor Fault Diagnosis Method Based on α -Grey Wolf Optimization-Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xuezhen Cheng ◽  
Dafei Wang ◽  
Chuannuo Xu ◽  
Jiming Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Similar Data ◽  
Grey Wolf ◽  
Sensor Fault ◽  
Grey Wolf Optimization ◽  
Diagnosis Method

Aimed to address the low diagnostic accuracy caused by the similar data distribution of sensor partial faults, a sensor fault diagnosis method is proposed on the basis of α Grey Wolf Optimization Support Vector Machine (α-GWO-SVM) in this paper. Firstly, a fusion with Kernel Principal Component Analysis (KPCA) and time-domain parameters is performed to carry out the feature extraction and dimensionality reduction for fault data. Then, an improved Grey Wolf Optimization (GWO) algorithm is applied to enhance its global search capability while speeding up the convergence, for the purpose of further optimizing the parameters of SVM. Finally, the experimental results are obtained to suggest that the proposed method performs better in optimization than the other intelligent diagnosis algorithms based on SVM, which improves the accuracy of fault diagnosis effectively.

Fault Diagnosis Method of Offset Printer Feeding Mechanism Based on Kernel Principal Component Analysis and K-Means Clustering

2015 ◽  
Vol 731 ◽  
pp. 395-400 ◽  
Author(s):  
Qian Qian Xu ◽  
Hai Yan Zhang ◽  
He Ping Hou ◽  
Zhuo Fei Xu
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
High Speed ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Detection And Diagnosis ◽  
Kernel Principal Component Analysis ◽  
Diagnosis Method ◽  
Detection And Diagnosis

The printing machine is a sort of large-scale equipment characterized by high speed and precision. A fault diagnosis method based on kernel principal component analysis (KPCA) and K-means clustering is developed to classify the types of feeding fault. The multidimensional and nonlinear data of printed image could be reduced by KPCA to make up the deficiency of the traditional K-means clustering method. In this paper, it is experimentally verified that the classification accuracy of the combined method is higher than the traditional clustering analysis method in feeding fault detection and diagnosis. This method provides a shortcut for the determination of fault sources and realizes multi-faults diagnosis of printing machinery efficiently

Sensitivity analysis of factors affecting motion reliability of manipulator and fault diagnosis based on kernel principal component analysis

Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Jing Yang ◽  
Lingyan Jin ◽  
Zejie Han ◽  
Deming Zhao ◽  
Ming Hu
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Component Analysis ◽  
Joint Clearance ◽  
Kernel Principal Component Analysis ◽  
Joint Rotation ◽  
Object A ◽  
Motion Performance ◽  
Flexibility Factor

Abstract As an important index to quantitatively measure the motion performance of a manipulator, motion reliability is affected by many factors, such as joint clearance. The present research utilized a UR10 manipulator as the research object. A factor mapping model for influencing the motion reliability was established. The link flexibility factor, joint flexibility factor, joint clearance factor, and Denavit–Hartenberg (DH) parameters were comprehensively considered in this model. The coupling relationship among the various factors was concisely expressed. Subsequently, the nonlinear response surface method was used to calculate the reliability and sensitivity of the manipulator, which provided an applicable reference for its trajectory planning and motion control. In addition, a data-driven fault diagnosis method based on the kernel principal component analysis (KPCA) was used to verify the motion accuracy and sensitivity of the manipulator, and joint rotation failure was considered as an example to verify the accuracy of the KPCA method. This study on the motion reliability of the manipulator is of great significance for the current motion performance, adjusting the control strategy and optimizing the completion effect of the motion task of a manipulator.

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