Title :
Full ceramic bearing fault diagnosis using LAMSTAR neural network
Author :
Yoon, J.M. ; He, Dawei ; Bin Qiu
Author_Institution :
Dept. of Mech. & Ind. Eng., Univ. of Illinois at Chicago, Chicago, IL, USA
Abstract :
In this paper, an integrated full ceramic bearing fault diagnostic system developed with acoustic emission (AE) sensors and a large memory storage and retrieval (LAMSTAR) artificial neural network (ANN) is presented. LAMSTAR is a newly developed and US patented neural network algorithm. The performance of the diagnostic system is compared with those implemented with other types of fault classification algorithms using laboratory seeded fault test data. The presented diagnostic system with LAMSTAR network achieved over 93% individual fault detection accuracies along with over 96% overall accuracy.
Keywords :
acoustic emission; ceramics; condition monitoring; fault diagnosis; information retrieval; machine bearings; mechanical engineering computing; neural nets; pattern classification; signal classification; AE sensors; LAMSTAR ANN; acoustic emission sensors; diagnostic system performance; fault classification algorithms; integrated full ceramic bearing fault diagnostic system; laboratory-seeded fault test data; large-memory storage-and-retrieval artificial neural network; Artificial neural networks; Sensors; AE sensors; ANN; LAMSTAR; acoustic emission sensors; artificial neural network; fault diagnosis; full ceramic bearing;
Conference_Titel :
Prognostics and Health Management (PHM), 2013 IEEE Conference on
Conference_Location :
Gaithersburg, MD
Print_ISBN :
978-1-4673-5722-7
DOI :
10.1109/ICPHM.2013.6621427
