Title :
The Research on Life Prediction of the Hoist Shaft Based on BP Neural Network
Author :
Yao Yunping ; Chen Qi ; Li Ying ; Dong Xinli
Author_Institution :
Lanzhou Univ. of Technol., Lanzhou, China
Abstract :
To evaluate and forecast lifespan of hoist shaft by ANN (Artificial Neural Networks), an evaluation model based on forecast lifespan is set up. A new theme is proposed to forecast remaining life of hoist shaft in which the mutation in cross-section of the bending stress acts as an input unit and remaining life acts as the output unit on the different condition. In order to forecast remaining life, it is essential to construct 5-9-1 BP (Back Propagation) network models. By studying the specific instance to forecast remaining life of 2JK hoist shaft as well as examine the accuracy of life prediction model.
Keywords :
backpropagation; hoists; mechanical engineering computing; neural nets; shafts; BP neural network; artificial neural networks; backpropagation network models; bending stress cross-section; hoist shaft life prediction; Artificial neural networks; Curve fitting; Elevators; Employee welfare; Neural networks; Predictive models; Shafts; Stress; Wires; Wounds; BP Artificial Neural Networks; Cross-section; Forecast Remaining; Hoist Shaft; component;
Conference_Titel :
Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on
Conference_Location :
Changsha City
Print_ISBN :
978-1-4244-5001-5
Electronic_ISBN :
978-1-4244-5739-7
DOI :
10.1109/ICMTMA.2010.77
