Dynamic concurrent kernel CCA for strip-thickness relevant fault diagnosis of continuous annealing processes

2018 ◽  
Vol 67 ◽  
pp. 12-22 ◽  
Author(s):  
Qiang Liu ◽  
Qinqin Zhu ◽  
S. Joe Qin ◽  
Tianyou Chai
2017 ◽  
Vol 9 (7) ◽  
pp. 168781401771370 ◽  
Author(s):  
Hai Xu ◽  
Ling-Li Cui ◽  
De-Guang Shang

The dynamic characteristics of the mill and the drive system are mutually coupled and affected closed-loop system. However, most research has considered only the vibration of the drive system or the vibration of the mill to determine the cause of the accident in the equipment condition monitoring and fault diagnosis process. Condition monitoring and fault diagnosis based on this type of approach can lead to misdiagnosis or missed diagnosis in determining faults in actual systems. So, in this study, a dynamic model of the coupling between a mill and its drive system was developed to study the interaction of the mill and the drive system with the goal of increasing the accuracy of diagnostic methods and to improve the quality of the rolled material. A nonlinear coupling dynamic model was formulated to represent the relation between the gearbox vibration amplitude and various time-varying parameters to study the effects of various parameters on the drive system vibration characteristic under unsteady lubrication. Simulations results showed that increasing the strip speed, the input strip thickness, or the output strip thickness or decreasing the lubricating oil temperature or the roller radius caused the vibration amplitude of the drive system to increase. The vibration frequency caused by variations in the strip inlet or outlet thickness can be transmitted to the drive system, and gear meshing frequency of the gearbox can be transmitted to the mill. Test data from an actual cold rolling mill verified the accuracy of the model. The model was shown to be capable of simulating the mutually coupled and affected mechanism between a mill and its drive system.


2011 ◽  
Vol 22 (12) ◽  
pp. 2284-2295 ◽  
Author(s):  
Qiang Liu ◽  
Tianyou Chai ◽  
Hong Wang ◽  
Si-Zhao Joe Qin

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.


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