머신러닝 및 AI를 활용한 볼밀 과부하 예측 방지
당사는 볼 밀 과부하 발생 30~40분 전에 이를 예측하는 머신러닝 시스템을 개발하여 작업자가 밀 부하 및 작동 매개변수를 조정할 시간과 가시성을 확보할 수 있도록 했습니다. 이전에는 연간 9.1일의 가동 중단과 과도한 에너지 소비로 어려움을 겪었지만, 새로운 예측 모델을 통해 생산 주기를 크게 안정화하고 가동 중단 시간을 30% 단축했으며 에너지 사용량을 530kWh에서 390~420kWh로 절감했습니다.
01 —The Impact
The results, up front.
30% reduction in downtime Energy usage decreased from 530 kWh to 390–420 kWh Annual impact of $0.85M–$1.16M Improved mill stability and operator confidence More predictable and efficient production cycles
02 — The Challenge
Where they started.
The mill operation lacked reliable predictive visibility. Operators had to react based on lagging indicators, leading to sudden overloads, emergency shutdowns, and increased wear. Energy consumption remained very high due to instability in mill loading. We started by analyzing sensor data, energy profiles, operating modes, and historical overload events. Our team engineered features across vibration, torque, feed rate, ball-loading, and acoustic signatures to build a robust predictive dataset. A supervised ML model was trained to identify precursor patterns signaling overload conditions. Once deployed, the model continuously monitored live streams and triggered operator alerts 30–40 minutes in advance through an integrated dashboard. We also optimized operating regimes by identifying ball-loading configurations that reduce overload probability. The project combined data science, operations optimization, and real-time monitoring to deliver sustained performance improvements.
04 — Approach & Methodology
How we got there.
The ML system surfaced hidden variable correlations previously impossible to detect manually. Overloads strongly correlated with specific amplitude behaviors in vibration signals and certain ball-loading ranges. Predictive insight enabled proactive load adjustments and optimized mill operating modes.
With the overload risk dramatically reduced, operators transitioned from firefighting to proactive cycle management — stabilizing output and reducing both energy intensity and mechanical stress.
05 —In Practice
Project samples.
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