caa a22 4500 477072801 CHVBK 20180405111431.0 cr unu---uuuuu 170330e19960301xx s 000 0 eng 10.1007/BF01413866 doi (NATIONALLICENCE)springer-10.1007/BF01413866 A hybrid fault diagnosis approach using neural networks [Elektronische Daten] [D. Yu, D. Shields, S. Daley] A hybrid fault diagnosis method is proposed in this paper which is based on the parity equations and neural networks. Analytical redundancy is employed by using parity equations. Neural networks then are used to maximise the signal- to- noise ratio of the residual and to isolate different faults. Effectiveness of the method is demonstrated by applying it to fault detection and isolation for a hydraulic test rig. Real data simulation shows that the sensitivity of the residual to the faults is maximised, whilst that to the unknown input is minimised. The simulated faults are successfully isolated by a bank of neural nets. Springer-Verlag London Limited, 1996 Artificial neural network nationallicence Fault diagnosis nationallicence Hydraulic system nationallicence Nonlinear system nationallicence Optimisation nationallicence Robustness nationallicence Yu D. Control Theory and Applications Centre, Coventry University, CV1 5FB, Coventry, UK aut Shields D. Control Theory and Applications Centre, Coventry University, CV1 5FB, Coventry, UK aut Daley S. Mechanical Engineering Centre, European Gas Turbines Ltd., Leicester, UK aut Neural Computing & Applications Springer-Verlag 4/1(1996-03-01), 21-26 0941-0643 4:1<21 1996 4 521 https://doi.org/10.1007/BF01413866 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF01413866 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Yu D. Control Theory and Applications Centre, Coventry University, CV1 5FB, Coventry, UK aut NATIONALLICENCE 700 1- Shields D. Control Theory and Applications Centre, Coventry University, CV1 5FB, Coventry, UK aut NATIONALLICENCE 700 1- Daley S. Mechanical Engineering Centre, European Gas Turbines Ltd., Leicester, UK aut NATIONALLICENCE 773 0- Neural Computing & Applications Springer-Verlag 4/1(1996-03-01), 21-26 0941-0643 4:1<21 1996 4 521 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer