Early diagnosis of processing faults based on machine online monitoring

Machining is the process that a kind of mechanical device change the dimensions or the performance of the workpiece, which has a great influence on the quality of the component. Manufacturing and processing enterprises always want to improve the passing rate and the life of the machining workpiece and reduce unnecessary costs during processing. This must strictly control machining process based mechanical process systems, however, due to non-ideal conditions of the actual process, the process is unstable, resulting in the quality of the product cannot be controlled during processing. In this paper, we propose a kind of machine fault pre-warning and diagnosis method based online testing of the process to solve this problem that machine fault can cause the quality problems of the workpiece during machining, collecting real-Time machine state parameters by the sensor signal, using signal analysis methods such as Fourier transform and wavelet analysis, and analyzing real-Time process monitoring data, and classifying data By KNN algorithm, and judging the machine working status and it's fault occurrence, and using LabVIEW to construct of the entire monitoring and controlling environment and to applied to the actual data, and processing and analyzing real-Time data in the machining process that can judge the state of the machine, so the faults of the machine can be early found, there, by reducing the failure rate of the workpiece. The research about online numerical control machine fault diagnosis not only real-Time judged part that the machine may have faults, but also optimized machining processes of the parts.