
实时监测传送带上的异常振动数据。
将光学传感器读数与已知数据进行交叉比对,以确认材料是否被遮挡或堵塞。
立即向PLC发出指令,停止受影响的传送带区域的运动。
通过集成仪表板,将维护警报通知发送给指定的工程团队。
在确认安全区域已清除后,启动机器人清理单位,以清除障碍物。

Ensure your environment meets these criteria before deployment.
Confirm all robotic units support the required sensor interfaces and communication protocols.
Ensure local network bandwidth supports high-frequency data streaming for edge processing.
Validate that jam detection triggers hard-stop signals compatible with existing safety circuits.
Establish baseline sensor calibration to distinguish between valid jams and environmental noise.
Complete mandatory training modules for operators on interpreting jam alerts and manual overrides.
Verify that safety protocols align with local industrial automation regulations and standards.
Map current jam points, analyze historical failure logs, and configure sensor thresholds.
Deploy detection logic to a single robot or zone to validate accuracy and recovery speed.
Expand implementation across the fleet while monitoring system load and false positive rates.
系统通过振动分析,在发生堵塞后 2 秒内即可检测到堵塞。
自动化处理比手动干预方法,平均停机时间减少了 40%。
Integrates LiDAR, vision, and force-torque sensors to detect physical obstructions with millisecond precision.
Processes jam patterns locally to ensure real-time decision-making without cloud dependency.
Directly interfaces with robot joints and brakes to execute safe stop or reverse maneuvers automatically.
Aggregates jam data across the fleet for predictive maintenance scheduling and trend analysis.
Mount sensors away from high-vibration areas to prevent signal interference during operation.
Tune sensitivity settings to ignore minor vibrations while capturing significant obstructions.
Implement a quarterly review cycle for AI model updates to adapt to changing operational environments.
Configure logs to retain jam events for at least 90 days to support root cause analysis and audits.