Title :
Research on Fault Diagnosis Method of the Tower Crane Based on RBF Neural Network
Author :
Liu, Xiaoyang ; Xue, Tingting ; Jiang, Qing ; Li, Jian
Author_Institution :
Sch. of Inf. Sci. & Eng., Shenyang Ligong Univ., Shenyang, China
Abstract :
As a result of the diversity of the tower crane faults, after the faults occurred, it is difficulty to accurately discriminate the fault type immediately. In this paper, the “clustering” of the RBF neural network effected on the input samples can be used to automatically realize the classification of the failure modes. Accordingly, the faults are diagnosed, and the specific example of the tower crane fault diagnosis in the MATLAB environment is given. The results show that the method can effectively and accurately diagnose the faults. Therefore, a new way is provided for the common fault diagnosis of tower crane.
Keywords :
cranes; fault diagnosis; mechanical engineering computing; radial basis function networks; MATLAB environment; RBF neural network; fault diagnosis method; radial basis function neural network; tower crane; Adaptation model; Artificial neural networks; Cranes; Fault diagnosis; Neurons; Poles and towers; Radial basis function networks; Fault diagnosis; RBF neural network; Tower crane;
Conference_Titel :
Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-1-4244-8432-4
DOI :
10.1109/AICI.2010.238
