Machine Failure Prediction
AutoMobi Engineering — CNC fuel-injector nozzle line
What you'll see here
Predicting equipment failure before it happens, so AutoMobi can move from reactive to proactive maintenance.
Business Context
AutoMobi Engineering Pvt. Ltd manufactures auto components on CNC machines. The fuel-injector nozzle shop has been hit with frequent unplanned equipment failures, disrupting throughput. Three months of hourly sensor readings (air & process temperature, rotational speed, torque, tool wear) have been collected to enable a data-driven predictive maintenance program.
Why a failure hurts
Unexpected machine failure
Production downtime
Lost output & revenue
Customer delivery delays
Objective
Build a binary classifier (Decision Tree) that predicts Failure (1) vs Normal (0)from sensor inputs. Goal: enable scheduling tool replacements before failure events, minimizing both downtime and unnecessary maintenance.
Key questions
1
Which sensor signals best predict an imminent failure?
2
How accurately can a decision tree separate failures from normal runs?
3
What is the relative importance of tool wear vs torque vs temperature?
4
Given live sensor readings, what is the failure probability right now?
5
What preventive-maintenance policy maximizes uptime?
AI: Why predictive maintenance matters here
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