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.
Highlights
Nowadays, the elevator has become an indispensable means of indoor transportation in people’s life
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
The elevator door system failure prediction, and timely failure type analysis, can effectively reduce the occurrence of the fault and reduce the accident caused by the gate system failure
Summary
The elevator has become an indispensable means of indoor transportation in people’s life. Among the common casualty accidents occurred in elevator operation process, the door system failure caused by accidents accounted for more than 70% [1]. The elevator door system failure prediction, and timely failure type analysis, can effectively reduce the occurrence of the fault and reduce the accident caused by the gate system failure. Wen et al 762 system during elevator operation process, to a certain extent, can monitor the abnormality of door system, but it cannot judge the type of failure in time; it needs to be improved. The paper [3] analyzed the causes of various faults through established elevator door system fault tree It can only be used in elevator door system design, but it cannot predict the fault. By comparing with the single BP neural network, the PSO-BP neural network is more accurate in the fault prediction of the elevator door system
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