TY - GEN
T1 - Distributed neuro-fuzzy feature forecasting approach for condition monitoring
AU - Zurita, Daniel
AU - Carino, Jesús A.
AU - Delgado, Miguel
AU - Ortega, Juan A.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/8
Y1 - 2014/1/8
N2 - The industrial machinery reliability represents a critical factor in order to assure the proper operation of the whole productive process. In regard with this, diagnosis schemes based on physical magnitudes acquisition, features calculation, features reduction and classification are being applied. However, in this paper, in order to enhance the condition monitoring capabilities, a forecasting approach is proposed, in which not only the current status of the system under monitoring in identified, diagnosis, but also the future condition is assessed, prognosis. The novelties of the proposed methodology are based on a distributed features forecasting approach by means of adaptive neuro-fuzzy inference system models. The proposed method is validated by means of an accelerated bearing degradation experimental platform.
AB - The industrial machinery reliability represents a critical factor in order to assure the proper operation of the whole productive process. In regard with this, diagnosis schemes based on physical magnitudes acquisition, features calculation, features reduction and classification are being applied. However, in this paper, in order to enhance the condition monitoring capabilities, a forecasting approach is proposed, in which not only the current status of the system under monitoring in identified, diagnosis, but also the future condition is assessed, prognosis. The novelties of the proposed methodology are based on a distributed features forecasting approach by means of adaptive neuro-fuzzy inference system models. The proposed method is validated by means of an accelerated bearing degradation experimental platform.
KW - Artificial intelligence
KW - Condition monitoring
KW - Feature extraction
KW - Fuzzy neural networks
KW - Machine learning
KW - Prognosis
KW - Remaining Useful Life
KW - Time domain analysis
UR - https://www.scopus.com/pages/publications/84946690236
U2 - 10.1109/ETFA.2014.7005180
DO - 10.1109/ETFA.2014.7005180
M3 - Conference contribution
AN - SCOPUS:84946690236
T3 - 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014
BT - 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014
A2 - Martinez Garcia, Herminio
A2 - Grau, Antoni
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2014
Y2 - 16 September 2014 through 19 September 2014
ER -