Title :
A Study on Method of Intelligent Fault Diagnosis in Large and Complex Device
Author :
Bai, Yiming ; Meng, Xianyao
Author_Institution :
Inf. Sci. & Technol., Dalian Maritime Univ., Dalian, China
Abstract :
Recently neural network is widely used in fault diagnosis. As the neural network method has the disadvantages of the slow convergence rate and the uncertain node number in neural network hidden layer, the results of fault diagnosis in complex devices are not satisfactory. This paper combines fuzzy logic and neural network, and presents a multilayer feed-forward fuzzy neural network with a serial structure for fault diagnosis. We take the fault diagnosis in ship diesel engine as an example to simulate. The results demonstrate that the method could improve the speed of diagnosis greatly and could diagnose and predict the device faults timely and rapidly. It can significantly improve the fault diagnosis to apply the method in large and complex devices.
Keywords :
fault diagnosis; fuzzy logic; neural nets; fuzzy logic; intelligent fault diagnosis; neural network hidden layer; slow convergence rate; Artificial neural networks; Buildings; Fault diagnosis; Fuzzy neural networks; Information science; Medical services; Research and development; fault diagnosis; fuzzy theory; large and complex device; neural network;
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
DOI :
10.1109/iCECE.2010.74