DIVERSE NEURAL-NET SOLUTIONS TO A FAULT-DIAGNOSIS PROBLEM

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 mea ns of a majority voter to form a system which achieved 100% generalisa tion to the test set. This performance is attributable to a reliance o n the software engineering concept of diversity. Following experimenta l evaluation of methods of creating diverse neural nets solutions, it was concluded that the best results should be obtained when data is ta ken from two different sensors (e.g. a pressure and a temperature sens or), or where this is not possible, when new data sets are created by subjecting a set of inputs to non-linear transformations. These conclu sions have far reaching implications for other neural net applications .