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Fuzzy theory is a forceful analytic tool to the faulty diagnosis of complicated system. A fuzzy clustering method is introduced on the basis of original fault diagnosis. The initial sort is acquired by fuzzy equivalent matrix and F-statistic, and the initial iterative matrix for the fuzzy c-means is also acquired. So it gets the most optimize class matrix and the fuzzy clustering center. The practical example shows the result calculated by fuzzy clustering is good agreement with the actual inspections, and the method can be applied to the faulty diagnosis of electric operation system of self-propelled rocket launcher. It can distinguish complicated faults more efficiently and accurately, and it has a good foreground. Foreword Electric actuator applied to rocket launcher is an extremely complicated system, to the extent that battle effectiveness of rocket launcher will be impaired directly in case of failure with electric operation system, which further leads to degraded technical supporting capacity of the army. In practice, a faulty rocket is usually disused by storing in depot, this not only prevents the army from routine drilling, but also substantially weakens its battle effectiveness. In this article, the theory of fuzzy clustering analysis is applied in a comprehensive manner, standard fault samples taken and data collected from practical operation for testing are used as classified samples, fault diagnosis of electric actuator for rocket launcher is conducted with transitive closure method based on fuzzy equivalent matrices and fuzzy C-means-based classification, which is proved to bring out crucial significance and remarkable military benefit in improving battle performance as well as technical condition and level of rocket launcher. Fundamental & Methodology for Fault Diagnosis with Fuzzy Clustering Basic Thinking.The method applied herein is that, by assuming normal operation of tested system, a set of data can be tested at first. Depending upon system functions, internal correlation exists among these sampling points, thus the method of fuzzy clustering may be applied in analyzing sampling points to derive standard functional model for normal system. Then, fuzzy clustering of tested values is conducted during actual operation of the system; in case of system fault, clustering center surely offsets standard model under normal condition, and the fault with those system functions as corresponding membership can be diagnosed on the basis of analysis and calculation of fuzzy distance. Obviously, this is a method of model identification in comparison with standard model. Clustering Method. 1) Data Standardization Assume domain of discourse { } 1 2 , , , n U x x x =  as the objects to be classified, where 1 1 x a = , 2 2 x a = , , 1 1 n n x a = , 1 1 1 n x b + = , 1 2 2 n x b + = , , 2 n n x b = , 1 a , 2 a , , 1 n a =standard fault sample, 1 b , 2 b , , 2 n b =fault sample to be tested; each object consists of m indexes of characteristic for expression of its properties, ( ) 1 2 , , , i i i im x x x x =  ( ) 1,2, , i n =  ; then, original data matrix is derived; International Power, Electronics and Materials Engineering Conference (IPEMEC 2015) © 2015. The authors Published by Atlantis Press 135 and then, standardization processing is performed with original data matrix derived. Usually, standard data is compressed to interval[0,1], with range transformation: { } { } { } ' 1

The Method for Fault Diagnosis of Electric Actuator for a Type of Self-propelled Rocket Launcher Based on the Fuzzy Clustering