A fault identification with fuzzy C-Mean clustering

algorithm based on improved ant colony algorithm (ACA) is

presented to avoid local optimization in iterative process of

fuzzy C-Mean (FCM) clustering algorithm and the difficulty in

fault classification. In the algorithm, the problem of fault

identification is translated to a constrained optimized

clustering problem. Using heuristic search of colony can find

good solutions. And according to the information content of

cluster center, it could merger surrounding data together to

cause clustering identification. The algorithm may identify

fuzzy clustering numbers and initial clustering center. It can

also prevent data classification from causing some errors.

Thus, applying in fault diagnosis shows validity of computing

and credibility of identification results.