The application provides a
machine tool
abnormality monitoring method based on context double-layer clustering and
incremental learning, and relates to the technical field of intelligent manufacturing and equipment health monitoring.The application introduces the concept of
processing context, establishes multiple micro-working condition clusters based on the multi-source
time sequence data of the
processing history parts, and constructs the health
baseline model of each micro-working condition cluster; based on the
time sequence feature sequence of the current
processing process, the target micro-working condition cluster of the current processing process is determined, and whether the current processing process of the
numerical control machine tool exists
abnormality is judged based on the health
baseline model of the target micro-working condition cluster; when an unknown new working condition appears, the micro-working condition cluster and the corresponding health
baseline model of the unknown new working condition are established; when the feature gradual drift of any one micro-working condition cluster appears, the health baseline model of the micro-working condition cluster is updated.When the
numerical control machine tool processes a new part after changing production, a new working
condition monitoring baseline can be generated without relying on manual labeling data and full amount retraining, which greatly enhances the flexible
adaptation ability of the
numerical control machine tool.