Title :
The short-term life prediction model of gearbox based on chaotic neural network
Author :
Chen, Xiao-hui ; Cui, Li-ming ; Li, Jun-xing
Author_Institution :
State Key Lab. of Mech. Transm., Chongqing Univ., Chongqing, China
Abstract :
Since faults of gearbox occurred randomly during the normal status, chaos theory was chosen to analyze the nonlinear characteristics of vibration acceleration signals for gearbox. The short-term prediction model of chaotic neural network for gearbox life was proposed based on chaotic time series. In the model, the chaotic time series phase space was reconstructed as the input vectors of neural network, and the predictable step of gearbox was set as the output vectors of neural network, then the short-term life of gearbox was obtained. The results of the simulation on the vibration acceleration signals of the test-gearbox showed that the model is more effective and accurate compared with the traditional neural network prediction methods.
Keywords :
chaos; gears; mechanical engineering computing; neural nets; signal processing; time series; vibrations; chaos theory; chaotic neural network; faults; gearbox; nonlinear characteristics; short-term life prediction model; vibration acceleration signals; Acceleration; Biological neural networks; Chaos; Delay; Predictive models; Time series analysis; Vibrations; Chaotic neural network; Chaotic time series; Gearbox; Phase space reconstruction; Short-term life prediction;
Conference_Titel :
Industrial Engineering and Engineering Management (IE&EM), 2011 IEEE 18Th International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-61284-446-6
DOI :
10.1109/ICIEEM.2011.6035367