using_ai_to_discover_explainable_industrial_analytics
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| using_ai_to_discover_explainable_industrial_analytics [2026/08/02 22:22] – [Why this matters] wikiadmin | using_ai_to_discover_explainable_industrial_analytics [2026/08/02 22:22] (current) – [The engineering process] wikiadmin | ||
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| The original paper investigates ML techniques because the data contains: | The original paper investigates ML techniques because the data contains: | ||
| - | * multiple machines | + | |
| - | * different cutting operations | + | * different cutting operations |
| - | * changing conditions over several years | + | * changing conditions over several years |
| - | * relatively few damaged examples | + | * relatively few damaged examples |
| Instead of training a model, we approached the data conversationally. | Instead of training a model, we approached the data conversationally. | ||
| Line 81: | Line 81: | ||
| Questions included: | Questions included: | ||
| - | * Does overall vibration amplitude separate good and bad tools? | + | |
| - | * Does energy move into particular frequency ranges? | + | * Does energy move into particular frequency ranges? |
| - | * Does the spindle speed matter? | + | * Does the spindle speed matter? |
| - | * Are the damaged tools exciting different mechanical behaviour? | + | * Are the damaged tools exciting different mechanical behaviour? |
| - | * Which measurements remain stable across different machines? | + | * Which measurements remain stable across different machines? |
| Most of these ideas were tested and rejected. | Most of these ideas were tested and rejected. | ||
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