Health Assessment of Armored Vehicles Based on K-means

In the field of weapon equipment fault diagnosis, a large amount of fault data is lacking, which makes it difficult to evaluate the condition of armored vehicles. In order to save the high cost of armored vehicle failure repair, reduce the redundant investment of manpower and material resources for armored vehicle maintenance, and improve the reliability of armored vehicle performance, a type of armored vehicle fire control system gyroscope is used as the research object. First, the rough set theory was used to reduce the data characteristics of the performance parameters of the gyroscope group of the fire control system, and then the initial clustering center was selected by the minimum variance to reduce the randomness of the initial clustering center and improve the accuracy. Finally, the reliability and accuracy of the method are verified by benchmark sample data and test data.