Founded in 1971 >
Chinese Sci-tech Core Periodicals >
British Science Abstracts (SA, INSPEC) Indexed Journals >
United States, Cambridge Scientific Abstract: Technology (CSA: T) Indexed Journals >
United States, Ulrich's Periodicals Directory(UPD)Indexed Journals >
United States, Cambridge Scientific Abstract: Natural Science (CSA: NS) Indexed Journals >
Poland ,Index of Copernicus(IC) Indexed Journals >
International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
Fax:
86-571-87239571
Add:
No.9 Gaoguannong,Daxue Road,Hangzhou,China
P.C:
310009
E-mail:
meem_contribute@163.com
Abstract: In order to improve the accuracy of safe operation of tower crane inspection results, avoid misjudgment, and improve the intelligence level of tower crane inspection, an improved learning vector quantization (LVQ) artificial neural network model was proposed to realize the intelligent inspection of safe operation of tower crane. Firstly, the randomly-selected test samples set was established for the whole equipment on the basis of the inspection samples of tower cranes on construction sites in recent years. Based on the safety technical standards and specifications of tower cranes, the inspection items were divided into the 15 common factors as the number of input layer of the neural network in the sample set. Then,the inspection data of 290 tower cranes were counted. For the connection of metal structure, working environment, main parts and mechanisms, the frequency of these three nonconformities was high. Finally, the conventional LVQ algorithm was improved on the evaluation model, and the LVQ classifier was trained by using the optimized characteristic data. Hence, based on the improved LVQ algorithm,an intelligent inspection was proposed. The classification and recognition experiments were carried out on 50 test samples. The research result shows that the improved LVQ algorithm can increase the accuracy of judgment, because both the qualified rate for qualified equipment and unqualified rate for the unqualified equipment can all reach 100% in the whole equipment inspection. Therefore, the improved LVQ algorithm can avoid misjudgment and realize the safe and intelligent inspection.
Key words: selfpropelled crane; operation safety status; safety technical standards and specifications; learning vector quantization (LVQ); artificial neural network model; LVQ classifier
-
- Chinese Core Periodicals
-
- Chinese Sci-tech Core Periodicals
-
- SA, INSPEC Indexed
-
- CSA: T Indexed
-
- UPD:Indexed
-
2010 Zhejiang Information Institute of Mechinery Industry
Technical Support：Hangzhou Bory science and technology
