Automatic fault isolation by cultural algorithms with differential influence

An evolutionary algorithm with a cultural mechanism of evolution influence for effectiveness and efficiency higher than classical genetic algorithms is proposed for industrial fault isolation. Moreover, the evolution influence is based on a differential concept in order to move toward better zones of the solution space by sensing the fitness gradient. The proposed cultural algorithm is designed in order to be portable and easily configurable in different diagnostic applications. On-field results of an industrial application to motor-vehicle fleet remote monitoring and automatic fault isolation of vehicle wear, operating danger, and fraud in a company that transports dangerous goods are shown.