Inspection Error Diagnosis Using Recipe and Log Learning

A learning device acts as an intermediary between the inspection device and the diagnosis system. The learning device receives log data from the inspection device and learns the relationship between log data patterns and error causes, then outputs diagnosed error causes to the diagnosis system. This intermediary approach improves diagnosis accuracy without requiring direct complex integration between all system components.

The learning device creates a virtual model or copy of the error diagnosis process by learning from historical log data and error cause information. Instead of directly analyzing complex raw data, the system uses this learned model to infer error causes, simplifying the actual diagnosis operation while maintaining high accuracy.

The learning device performs preliminary learning actions by training on historical log data and error cause information before actual diagnosis operations. This pre-learning process enables the system to quickly diagnose error causes in real-time operations without requiring extensive processing of multiple data types during actual use, thus maintaining high reliability while reducing processing time.

The system implements feedback mechanisms where diagnosis results are fed back into the learning device to continuously improve the learned model. This feedback loop allows the system to refine its error cause specification accuracy over time based on accumulated data, improving reliability without proportionally increasing processing time as the model becomes more efficient.