Fault Prediction of Elevator Door System Based on PSO-BP Neural Network
- 1 Chongqing Special Equipment Inspection and Research Institute, Chongqing, China
- 2 Chongqing Special Equipment Inspection and Research Institute, Chongqing, China
- 3 Chongqing Special Equipment Inspection and Research Institute, Chongqing, China
- 4 Chongqing Special Equipment Inspection and Research Institute, Chongqing, China
Abstract
Nowadays, the elevator has become an indispensable means of indoor transportation in people’s life, but in recent years this kind of traffic tools has caused many casualties because of the gate system fault. In order to ensure the safe and reliable operation of the elevator, the failure of elevator door system was predicted in this paper. Against the fault type of elevator door system: elevator door opened, excessive vibration when elevator door opened or closed, elevator door did not open or closed when reached the specified level. Three fault types were used as the output of the prediction model. There were 8 reasons for the failure, used them as input. A model based on particle swarm optimization (PSO) and BP neural network was established, using MATLAB to emulation; the results showed that: PSO-BP neural network algorithm was feasible in the fault prediction of the elevator door system.
- Lin, D.Y. (2012) Failure Cause Analysis and Prevention Measures in the Elevator Door System. Friend of Science Amateurs, 7, 19-20.
- Zhen, S.J. (2009) State Monitor and Experimental Research of Elevator Door System. Shanghai Jiao Tong University, Shanghai.
- Xue, T., Peng, Q.F., Huang, G.J., et al. (2015) Fault Analysis and Evaluation Method of Elevator Door System Based on Fault Tree. Automation & Information Engineering, 6, 34-36.
- Zhai, Z.Z. (2015) DSP-Controlled Elevator Door-Motor System. Guangdong University of Technology, Guangzhou.
- Li, J.F., Qu, Z.W., Dou, L.Q., et al. (2009) Study of the Fault Predication for Elevator Gate System Based on Neural Network. Journal of Tianjin University of Technology, 1, 8-10.
- Shao, Y.X., Chen, Q. and Zhang, D.M. (2008) The Application of Improved BP Neural Network Algorithm in Lithology Recognition. Lecture Notes in Computer Science, 5370, 342-349. http://dx.doi.org/10.1007/978-3-540-92137-0_38
- Ince, D. and Sofu, A. (2013) Estimation of Lactation Milk Yield of Awassi Sheep with Artificial Neural Network Modeling. Small Ruminant Research, 1.
- Lu, Q.S. and Wang, S.Q. (2011) BP Neural Network Optimization Algorithm Based on Genetic-Stimulated Annealing. Computer and Modernization, 6, 91-94.
- Yuan, C.R. (1999) Artificial Neural Network and Application. Tsinghua University Press, Beijing.
- Poli, R., Kennedy, J. and Blackwell, T. (2007) Particle Swarm Optimization. Swarm Intelligence, 1, 33-57. http://dx.doi.org/10.1007/s11721-007-0002-0