The overall aim of this research was to design, configure and validate a system
which was capable of on-line performance monitoring and fault diagnosis of a
diesel engine. This thesis details the development and evaluation of a
comprehensive engine test facility and automated engine performance monitoring
package. Results of a diesel engine fault study were used to ascertain commonly
occurring faults and their realistic severities are discussed. The research shows how
computer simulation and rig testing can be applied to validate the effects of faults
on engine performance and quantify fault severities. A substantial amount of engine
test work has been conducted to investigate the effects of various faults on high
speed diesel engine performance. A detailed analysis of the engine test data has led
to the development of explicit fault-symptom relationships and the identification of
key sensors that may be fitted to a diesel engine for diagnostic purposes. The
application of a neural network based approach to diesel engine fault diagnosis has
been investigated. This work has included an assessment of neural network
performance at engine torques and speeds where it was not trained, noisy engine
data, faulty sensor data, varying fault severities and novel faults which were similar
to those which the network had been trained on. The work has shown that diagnosis
using raw neural network outputs under operational conditions would be inadequate.
To overcome these inadequacies a new technique using an on-line diagnostic
database incorporating 'weight adjusting' and 'confidence factor' algorithms has
been developed and validated. The results show a neural network combined with an
on-line diagnostic database can be successfully used for practical diesel engine fault
diagnosis to offer a realistic alternative to current fault diagnosis techniques.The Ministry Of Defenc
