caa a22 4500 47707295X CHVBK 20180405111431.0 cr unu---uuuuu 170330e19961201xx s 000 0 eng 10.1007/BF01413820 doi (NATIONALLICENCE)springer-10.1007/BF01413820 Diverse neural net solutions to a fault diagnosis problem [Elektronische Daten] [A. Sharkey, N. Sharkey, G. Chandroth] The development of a neural net system for fault diagnosis in a marine diesel engine is described. Nets were trained to classify combustion quality on the basis of simulated data. Three different types of data were used: pressure, temperature and combined pressure and temperature. Subsequent to training, three nets were selected and combined by means of a majority voter to form a system which achieved 100% generalisation to the test set. This performance is attributable to a reliance on the software engineering concept of diversity. Following experimental evaluation of methods of creating diverse neural nets solutions, it was concluded that the best results should be obtained when data is taken from two different sensors (e.g. a pressure and a temperature sensor), or where this is not possible, when new data sets are created by subjecting a set of inputs to non-linear transformations. These conclusions have far reaching implications for other neural net applications. Springer-Verlag London Limited, 1996 Diversity nationallicence Engines nationallicence Fault diagnosis nationallicence Neural nets nationallicence Software engineering nationallicence Sharkey A. Department of Computer Science, University of Sheffield, 211 Portobello Street, Regent Court, S1 4DP, Sheffield, UK aut Sharkey N. Department of Computer Science, University of Sheffield, 211 Portobello Street, Regent Court, S1 4DP, Sheffield, UK aut Chandroth G. Department of Computer Science, University of Sheffield, 211 Portobello Street, Regent Court, S1 4DP, Sheffield, UK aut Neural Computing & Applications Springer-Verlag 4/4(1996-12-01), 218-227 0941-0643 4:4<218 1996 4 521 https://doi.org/10.1007/BF01413820 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF01413820 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sharkey A. Department of Computer Science, University of Sheffield, 211 Portobello Street, Regent Court, S1 4DP, Sheffield, UK aut NATIONALLICENCE 700 1- Sharkey N. Department of Computer Science, University of Sheffield, 211 Portobello Street, Regent Court, S1 4DP, Sheffield, UK aut NATIONALLICENCE 700 1- Chandroth G. Department of Computer Science, University of Sheffield, 211 Portobello Street, Regent Court, S1 4DP, Sheffield, UK aut NATIONALLICENCE 773 0- Neural Computing & Applications Springer-Verlag 4/4(1996-12-01), 218-227 0941-0643 4:4<218 1996 4 521 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer