Considering the lack of the fault data in the real production process

support vector machine (SVM)

which fits the small sample problem was adopted to diagnose the faults of Stone & Webster Ultra-selective Cracking (USC) naphtha cracking furnace.To eliminate the disturbances from the high dimensional data as well as system noises

a novel adaptive quantum ant colony optimization (AQACO) algorithm was proposed to select the fault features based on the classical ACO with the introduction of quantum bit.The results of numerical simulation experiments showed that the proposed AQACO had better global optimization ability.The results of diagnosis of cracking furnace sensor faults demonstrated that AQACO could find the fault features quickly and exactly

which effectively improved the fault diagnosis performance of SVM in terms of correctness and robustness.