[KDD 2020] A Self-Evolving Mutually-Operative Recurrent Network-based Model for Online Tool Condition Monitoring in Delay Scenario
Aug 13, 20208 views
With the increasing demand of product supply, manufacturers are,in urgent need of online tool condition monitoring (TCM) without compromising with the maintenance cost in terms of time as,well as man-power requirement. However, the existing machine,learning models for TCM are mostly offline and not suitable for the,non-stationary environment of the machining settings. Moreover,,the access of the ground truth always imposes a shutdown of the,machining process and the existing models are severely affected by,such delay in receiving labelled samples. In order to tackle these,issues, we propose SERMON as a novel learning model based on a,pair of,self-evolving mutually-operative recurrent neural networks,.,The proposed SERMON is well-equipped with features for automated and real-time monitoring of machine fault status even in,the finite/infinite,label delay,scenario. The experimental evaluation of SERMON using real-world dataset on,3D-printing,process,demonstrates its effectiveness in online fault detection under nonstationary as well as delayed label context of the machining process.,Additional comparative study on large-scale benchmark streaming,datasets further exhibits the scalability power of SERMON.