Conveyors — AI for belt health, loading and energy
Continuous monitoring of belt condition, idler health, tracking, and load distribution. AI flags splice degradation days ahead of failure, optimizes speed to load profile, and cuts idling time across multi-kilometer overland conveyors.
概要
Conveyors are the arteries of a mine. When one stops, everything downstream stops with it. A single splice failure on an overland belt can cost days of production and weeks of repair logistics.
Most conveyor monitoring today is vibration alarms and visual inspection during planned stops. Belt health degrades continuously; alarm-based monitoring catches only the end of the curve.
BrainiAll AI Autopilot fuses camera imagery of belt surface and splices, strain-gauge data, drive motor current, and load-cell feeds into one health score per belt segment. It predicts splice and cover failures days ahead and recommends loading/speed adjustments that cut kWh per ton moved.
Autopilotの機能
連続的・多変数制御 — 単一ループPIDではありません。アドバイザリ層アーキテクチャにより安全性はそのまま維持されます。
Splice health prediction
Camera + strain-gauge fusion flags splice degradation days ahead of failure.
Idler & pulley monitoring
Acoustic + thermal signatures isolate a single bad idler along kilometers of belt.
Load-profile optimization
Coordinates feeders to keep belts centered and trough-loaded.
Variable-speed drive tuning
Belt speed adapts to actual load — cuts energy per ton moved.
Spillage detection
Computer vision flags spillage and misalignment in near real time.
継続的に調整される変数
AIはサークルのすべてのセンサーを読み、最適なセットポイント組み合わせをリアルタイムで解決します。
- Belt speed (m/s)VSD set to load.
- Load distribution (kg/m)Uniform trough loading.
- Drive motor currentAbnormal draw flags issue.
- Strain (splice, support)Early-warning window.
- Belt tracking error (mm)Edge wear and spillage risk.
A single unplanned overland conveyor stop can cost $500K-$2M depending on mine size. Predicting splice failure a few days ahead is usually sufficient to schedule the fix during a planned window — saving the full unplanned loss.
