HEALTHY CONDITION RECOGNITION FOR QUAYSIDE CONTAINER CRANE REDUCER BASED ON WEIBULL AND GG FUZZY CLUSTERING

Abstract

A method based on Weibull distribution and GG fuzzy clustering is studied and proposed in order to solve the issue of healthy condition recognition for quayside container crane（QCC） reducer. Using envelope method to denoise data which caused by the complexity conditions firstly. Then the scale parameters and shape parameters are created using Weibull distribution fitting, which could show change characteristics of collected samples quantitatively. In order to recognize the reducer condition further, the GG fuzzy clustering method is used to divide the different stages of the healthy condition, realizing recognition for reducer healthy. Test data from NetCMAS system is adopted in living analysis, FCM and GK clustering algorithm are analyzed for contrast. The results show that the method proposed in this paper is effective and has better clustering effect.

Keywords

• Quayside container crane reducer;Weibull distribution;GG fuzzy clustering;Condition recognition