R2R Equipment Performance Decline Prediction Technology Based On Adaptive Fuzzy Clustering Method

The flexible film is an anisotropic material.The deformation of flexible film has uncertainty,and the external interference or the change of performance of the manufacturing equipment will affect the deformation of the flexible film,and the material is prone to wrinkle,damage and other quality problems.The performance of R2 R manufacturing equipment is a bottleneck that restricts large-scale manufacturing.How to make R2 R manufacturing equipment zero-failure and intelligent,and how to ensure the equipment operating rate and the expected product output rate at the lowest price has been widely received attention from the industry and academia.However,in the current method of predicting malfunction of R2 R processing equipment for flexible materials,most of them focus on the identification of malfunction modes,which is a passive maintenance method.It has an adverse effect on the maximization of production equipment efficiency.The performance degradation prediction of the equipment is a breakthrough in the thinking mode of this research field.It is more inclined to predict the performance degradation in the entire life cycle of the equipment and comprehensively evaluate the operation conditions of the processing equipment to prevent the equipment from malfunctioning.This study can be used to guide the actual production,and to avoid the failure of the equipment performance to make the flexible material surface wrinkles,or even broken.It's not to scrap the entire roll of flexible materials,and it improves the quality of the film,reduces the rate of material waste,and increases the company's production efficiency.According to the previous research situation,the main research methods of the paper were determined,and the research progress of domestic and foreign related technologies based on fuzzy clustering equipment performance degradation prediction was discussed.The main work of this paper includes:(1)Briefly introduced the research background and significance of the paper,outlined the development conditions of the roller shaft equipment malfunction prediction and health management system(PHM)and domestic and foreign research status and summarized the main research contents of the paper.(2)The original vibration data of rollers are denoised by least square method,extracted the time domain feature parameters of the roller shaft vibration data after noise reduction processing,comprehensively analyzed the time domain feature parameters and selected a accurate reflection of the characteristics of the data changes as a characteristic indicator that describes the performance degradation of R2 R processing equipment.(3)An adaptive fuzzy clustering method(AFCM)based performance decay prediction model is proposed,and described the general steps of AFCM-based equipment performance degradation modeling in detail,and then divide the input roll axis feature parameters by the means of AFCM,and then used it to reflect the performance degradation of the device through the value of the membership function between the roller axis vibration data collected in real time and the fuzzy prototype.