CGHMM based fault identification for single point diamond flycutting machines

Abstract

The ultra-precision single point diamond flycutting method is commonly used to produce fine optical surfaces. But the motion errors of machine tool are very weak, it is hard to achieve the error detection for single point diamond flycutting machines. To solve this problem, different measuring data such as vibrations and temperatures of different components were collected synchronously, and then we established the fault identification method based on hidden Markov model (HMM) to analyse the acquired measuring signals. The method includes signal analysis of raw data, fault recognition based on continuous Gaussian mixture density hidden Markov model (CGHMM) and information fusion. It combines the advantages of HMM and information fusion, to identify fault information accurately. Results show that the identification rates of the temperature signals to classification status by GRMS can reach 92% and vibration signals are little related to the surface quality of the workpieces classified by GRMS.