With the increasingly fierce competition in the market,the small batch production mode has become an im-portant production method.Aiming at the characteristics of small batch production and the problem that a large number of pre-experiments are required in existing research,a state monitoring method of machining process based on recurrence analysis and machine learning was proposed.The power signal of a small number of trials processing of different workpieces was collected through processing experiments;the preprocessed power data was input into the deep belief network for training,and the trained workpiece recognition model was obtained by optimizing genetic algorithm;the recurrence analysis and iterative self-organizing data analysis were performed to obtain the state mo-nitoring model.Finally,the case study verified the effectiveness of the condition monitoring method,in which the workpiece identification accuracy was 99.3％,and the condition monitoring accuracy was 98％.
关键词
小批量/递归分析/深度信念网络/迭代自组织数据分析/状态监测
Key words
small batch/recurrence analysis/deep belief network/iterative self-organizing data analysis/state moni-toring
维普
万方数据