Open AccessFault Prediction of Elevator Door Lock Based on MPGA-BP Algorithm
Author(s) -
Jun Qiu,
Bei Qiu,
Leijing Yang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2179/1/012030
Subject(s) - lock (firearm) , artificial neural network , genetic algorithm , backpropagation , elevator , fault (geology) , algorithm , population , computer science , premature convergence , generalization , convergence (economics) , real time computing , engineering , artificial intelligence , machine learning , mathematics , structural engineering , mathematical analysis , demography , seismology , sociology , geology , economic growth , economics
The door lock is one of the elevator fault prone parts, and the fault may further lead to personal injury accidents. In the process of elevator operation, the door lock acts frequently, resulting in a large amount of operation data. Using big data method, the fault prediction of elevator door lock could be realized, so as to prevent and reduce accidents. BP (Back Propagation) neural network algorithm and GA-BP (Genetic Algorithm) were used as door lock fault prediction algorithms, but there were some defects in performance. BP algorithm was easy to fall into local minima and the convergence speed was uncertain, while GA-BP algorithm would reduce the genetic diversity of the population. In this paper, we use MPGA (Multiple Population Genetic Algorithm) to improve the initial weight threshold of BP algorithm, adjust the neural network, and establish a door lock fault prediction model. The simulation results show that MPGA-BP model greatly improves the generalization ability and prediction accuracy of BP neural network, compared with traditional BP and GA-BP model.