In view of the problems of lack of clear sample labels,noise in samples and lack of samples in the case of small and medium-sized samples in actual production,this paper proposes equipment health status analysis and life prediction model based on improved ant colony optimization K-Means and Multi Classification Self Adding(SVM).First,based on the fuzzy data set,the data are classified for the first time according to the traditional SVM,and the first classification result is obtained.Then,the improved K-Means algorithm based on Ant Colony Algorithm is used to cluster the data set after the initial classification,so as to get more device health status labels in different states.Secondly,the noise scale coefficient is established,and the data set distribution is optimized by introducing the unbalanced scale standard and the adaptive addition rule,so as to enrich the sample size of deficient tags without considering the influence of noise.On this basis,the SVM set is introduced according to the number of clustering categories to realize the multi classification processing of the data set.Thirdly,the future health trend of the equipment is evaluated by fitting the root mean value of vibration and the change trend of residual life.Finally,an example shows that the ACO-K-Means combined with MCS-SVM model proposed in this paper has good results in equipment health classification and future life prediction under the unbalanced data of small noise samples.
State RecognitionSVMK-MeansRemaining Life PredictionUnbalanced DataNoise Data
NETL
NSTL
万方数据
