Abstract: The performance of each component in the system is defined by performance parameters x, which are related to measurement parameters z expressed as a function h( ) of the performance parameters x and operating parameters w. The method comprises: (a) setting an assumed maximum number of fault affected components and defining a series of fault classes corresponding to possible outcomes in terms of faulty components, (b) creating an initial population of strings for each fault class, each comprising a plurality of elements corresponding to the performance and operating parameters (c) optimising for each class an objective function J(x,w) which gives a measure of the consistency between measured values and calculated values of the measurement parameters calculated using said function of the performance parameters and the operating parameters h(x,w), and (d) selecting the class having the best value of the objective functions.

Abstract: Methods and systems for fault diagnosis are described. The methods are in particular, although not necessarily exclusively, for the detection of faults in multi-component systems, typically power plant, whose performance is characterised by a series of indirectly measurable performance parameters. The methods proposed are based on “optimisation techniques”, involving the optimisation of an objective function to obtain an estimate of performance parameters and operating parameters. In one aspect, a method is proposed that takes account of measurement biases. In another aspect, a method for optimising an objective function is proposed, which adopts a unique Evolution Program approach in which a population of possible solutions is sub-divided into a number of groups.

Abstract: The performance of each component in the system is defined by performance parameters x, which are related to measurement parameters z expressed as a function h() of the performance parameters x and operating parameters w. The method comprises: (a) setting an assumed maximum number of fault affected components and defining a series of fault classes corresponding to possible outcomes in terms of faulty components, (b) creating an initial population of strings for each fault class, each comprising a plurality of elements corresponding to the performance and operating parameters (c) optimising for each class an objective function J(x,w) which gives a measure of the consistency between measured values and calculated values of the measurement parameters calculated using said function of the performance parameters and the operating parameters h(x,w), and (d) selecting the class having the best value of the objective functions.