A genetic algorithm approach to measurement prescription in fault diagnosis

To fully discriminate among all possible diagnoses in a fault diagnosis task, one needs to take measurements from the system being diagnosed. The primary effects of taking one measurement in diagnosis based on first principles were presented in A. Reiter [Artificial Intelligence (32) (1987) 57–95] and a more detailed, formal account was given in A. Hou [Artificial Intelligence (65) (1994) 281–328]. However, the order in which measurements are to be taken is an issue. We propose a genetic algorithm to determine a good measurement order for a diagnosis task. The method applies operators such as selection, crossover, and mutation to evolve an initial population of measurement sequences. The quality of a measurement sequence is evaluated based on the cost taken for the measurement sequence to find the final diagnosis. Experiments on testing circuits have shown that the quality of measurement sequences is greatly improved after evolution.