Title :
Applications of Information Fusion Based on Fuzzy Neural Network to Rotating Machinery Fault Diagnosis
Author :
Jin, Liu ; Shufen, Wang
Author_Institution :
Shijiazhuang Railway Inst., Shijiazhuang, China
Abstract :
The faults of key equipments in continuous production system often affect the entire production system and result in major economic loss, so fault diagnosis technology of machinery has become an important research direction in the field of machinery and measurement. This paper takes rotating machinery vibration as the main research object and research the prediction method of fault diagnosis of rotating machinery based on vibration. Aiming at the typical faults of rotating machinery, it introduces the extraction of fault characteristic parameters and data processing methods, and constructs a model of fault diagnosis based on fuzzy neural network and process network optimization. The validity of the model is verified based on simulation of production process.
Keywords :
fault diagnosis; fuzzy neural nets; machinery; mechanical engineering computing; sensor fusion; vibrations; data processing methods; fault diagnosis; fusion information; fuzzy neural network; machinery vibration; rotating machinery; Continuous production; Data mining; Data processing; Economic forecasting; Fault diagnosis; Fuzzy neural networks; Loss measurement; Machinery; Prediction methods; Production systems;
Conference_Titel :
Intelligent Information Technology Application Workshops, 2009. IITAW '09. Third International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-1-4244-6420-3
Electronic_ISBN :
978-1-4244-6421-0
DOI :
10.1109/IITAW.2009.103