Open AccessFault diagnosis method for attached lifting scaffold based on support vector machine
Author(s) -
Luo Shaoxuan,
Qiao Aimin,
Tang Qingguo
Publication year - 2020
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.1177
Subject(s) - support vector machine , computer science , fault (geology) , scaffold , artificial neural network , position (finance) , displacement (psychology) , process (computing) , least squares support vector machine , particle swarm optimization , artificial intelligence , algorithm , simulation , psychology , finance , database , seismology , economics , psychotherapist , geology , operating system
When the attached lifting scaffold fails in the process of high‐altitude lifting operation, it mainly relies on instrument alarm and operator's experience to make simple judgment and treatment, which has great potential safety hazards and accident risks. In this study, a fault diagnosis method of scaffolding jamming based on improved particle swarm optimisation least squares‐support vector machine (LS‐SVM) is proposed, which can automatically diagnose the cause of the fault. The experimental results on construction site show that the method can accurately judge the overall overload, specific position overload, overall under‐load, local deformation and displacement asynchronism faults occurring during the operation of scaffolding, and has higher diagnostic accuracy than the methods of back propagation neural network and standard SVM. It is significant to improve the safety level and operation efficiency of attached scaffolding.