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A novel supervised particle swarm optimization (S-PSO) classification algorithm is proposed for fault diagnosis. In order to improve the accuracy of fault diagnosis and obtain the global optimal solutions with a higher probability, two strategies, i.e. a hybrid particle position updating strategy and a fixed iteration interval intervention updating strategy, are designed to balance the effect of the local and the global search. These methods increase the diversity of particles, expand the particles ability of searching the entire solution space, and guide the particles adaptively jumping out of the local optimal area. Meanwhile, based on the shorter intra-class distance, longer inter-class distance and maximum classification accuracy of training samples, a fitness function is designed to constraint the output optimal class centers. Experimental results demonstrate that the proposed S-PSO classification algorithm can overcome the problems in the classical clustering algorithms, which only consider the similarity of data instead of their physical meanings. The comparison on GE90 engine borescope image texture feature classification is also conducted. The results show that the performance of S-PSO classification algorithm is robust. Its classification accuracy is higher than those of popular methods, including support vector machine (SVM), neural network, Bayesian classifier, and k-nearest neighbor (k-NN) algorithm. [ABSTRACT FROM AUTHOR]