caa a22 4500 475834100 CHVBK 20180406123849.0 cr unu---uuuuu 170329e20000701xx s 000 0 eng 10.1007/s005210070026 doi (NATIONALLICENCE)springer-10.1007/s005210070026 A Multi-Net System for the Fault Diagnosis of a Diesel Engine [Elektronische Daten] [A. J. C. Sharkey, G. O. Chandroth, N. E. Sharkey] A multi-net fault diagnosis system designed to provide an early warning of combustion-related faults in a diesel engine is presented. Two faults (a leaking exhaust valve and a leaking fuel injector nozzle) were physically induced (at separate times) in the engine. A pressure transducer was used to sense the in-cylinder pressure changes during engine cycles under both of these conditions, and during normal operation. Data corresponding to these measurements were used to train artificial neural nets to recognise the faults, and to discriminate between them and normal operation. Individually trained nets, some of which were trained on subtasks, were combined to form a multi-net system. The multi-net system is shown to be effective when compared with the performance of the component nets from which it was assembled. The system is also shown to outperform a decision-tree algorithm (C5.0), and a human expert; comparisons which show the complexity of the required discrimination. The results illustrate the improvements in performance that can come about from the effective use of both problem decomposition and redundancy in the construction of multi-net systems.: Springer-Verlag London Limited, 2000 Keywords:Artificial neural networks nationallicence Combustion nationallicence Condition monitoring nationallicence Engine nationallicence Ensembles nationallicence Fault diagnosis nationallicence Modular nationallicence Pressure nationallicence Sharkey A. J. C. Department of Computer Science, University of Sheffield, Sheffield, UK, GB aut Chandroth G. O. Department of Computer Science, University of Sheffield, Sheffield, UK, GB aut Sharkey N. E. Department of Computer Science, University of Sheffield, Sheffield, UK, GB aut https://doi.org/10.1007/s005210070026 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s005210070026 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sharkey A. J. C. Department of Computer Science, University of Sheffield, Sheffield, UK, GB aut NATIONALLICENCE 700 1- Chandroth G. O. Department of Computer Science, University of Sheffield, Sheffield, UK, GB aut NATIONALLICENCE 700 1- Sharkey N. E. Department of Computer Science, University of Sheffield, Sheffield, UK, GB aut Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer