具备自动特征提取能力的智能监测系统

为了减少加工状态监测系统开发的时间和成本,提出自动敏感特征提取方法,自动选择合适的传感器和信号处理技术来提取出"敏感特征".针对高速铣削过程中的刀具磨损监测,采用切削力、振动、声音和声发射传感器来采集信号,并运用时域、频域和小波分析技术对信号进行处理.试验结果表明:该方法可自动地进行传感器和信号处理技术的选择,提取出的敏感特征适合于自学习监测系统应用.

An intelligent monitoring system with the capability of automated features selection

To reduce the development time and cost,an automatic sensory feature selection method is proposed for the systematic design of condition monitoring systems for machining operations.Force,acceleration,sound and acoustic emission sensors were used in high-speed milling operations.The time domain,frequency domain and wavelet analysis technique were employed to process the signals.Gradual tool wear was used for evaluating the proposed self-learning automated sensory feature selection approach.The experimental results show that the suggested algorithm can be applied in an automated,self-learning monitoring system for the selection of the most sensitive sensors.