DocumentCode
531858
Title
Notice of Retraction
Establishment and analysis of vibration system based on time series models
Author
Cao Xin-Yan ; Liu Hong-fei
Author_Institution
Coll. of Electron. Inf. Eng., Univ. of Changchun, Changchun, China
Volume
8
fYear
2010
fDate
22-24 Oct. 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Vibration signals are the important information for system failures. Forecasting the trend of vibration signals is an important content of condition monitoring and fault diagnosis. A novel time series analysis is presented to analyze and forecast nonlinear random vibration signals. Mathematical models are established to describe vibration signals. First, the non-stationary vibration signals acquired in the field are transformed to stationary time series. Second, the time series models are constructed from the selected reference signals, and nonlinear least square method is used to estimate the model´s parameters. Then, the vibration signals are forecasted using the models. The application results show that the models can simulate time series of vibration signals quite well with good accuracy and meet the need of forecasting.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Vibration signals are the important information for system failures. Forecasting the trend of vibration signals is an important content of condition monitoring and fault diagnosis. A novel time series analysis is presented to analyze and forecast nonlinear random vibration signals. Mathematical models are established to describe vibration signals. First, the non-stationary vibration signals acquired in the field are transformed to stationary time series. Second, the time series models are constructed from the selected reference signals, and nonlinear least square method is used to estimate the model´s parameters. Then, the vibration signals are forecasted using the models. The application results show that the models can simulate time series of vibration signals quite well with good accuracy and meet the need of forecasting.
Keywords
condition monitoring; fault location; forecasting theory; least squares approximations; time series; vibrations; condition monitoring; fault diagnosis; forecast nonlinear random vibration signals; forecasting; nonlinear least square method; system failures; time series; vibration system; Artificial neural networks; Deformable models; forecast; model; parameter estimation; time series; vibration signal;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location
Taiyuan
Print_ISBN
978-1-4244-7235-2
Type
conf
DOI
10.1109/ICCASM.2010.5619036
Filename
5619036
Link To Document