AI Predictive Maintenance Safeguarding
24/7 Continuous Automated Production
In modern industrial automated production lines, critical equipment—such as robotic arms and conveyor systems
integrates tri-axial MEMS sensors with AI algorithms to deliver precise localization across 21 equipment failure modes, slashing O&M labor hours by over 40% and saving tens of millions in downtime costs.
Are You Facing These Challenges on Your Automated Production Line?
Sudden Critical Asset Failures Halting Entire Lines
Highly interconnected automated lines mean a single component failure can paralyze the whole production line, severely impacting capacity and delivery schedules.
Hard-to-Detect Early Anomaly Signs
Initial symptoms like bearing degradation, motor imbalance, and mechanical looseness are subtle and easily missed during routine operations, often going unaddressed until complete breakdown occurs.
Over-Reliance on Manual Walkthroughs
Periodic physical inspections fail to provide real-time health visibility and are prone to human misjudgment due to varying technician experience levels.
Escalating Costs from Unplanned Downtime
Unexpected outages inflate repair bills, delay order fulfillment, compromise product yield, and trigger severe capacity losses.
Data Blind Spots Hindering Predictive Servicing
Without operational trend data, teams are forced to rely on rigid calendar maintenance—resulting in costly over-servicing or risky under-servicing.
AI Vibration Monitoring:
Early Anomaly Warning
By leveraging vibration telemetry for 24/7 health tracking combined with real-time AI analysis, the system detects fault signatures long before failure strikes. This empowers enterprises to minimize unplanned downtime, maximize equipment reliability, and build stable, high-efficiency smart automated production lines.
From Reactive Firefighting to Proactive Prevention
Prognosis 21 Failure Mode Diagnostics for Fully Automated Fabs
- 💰 NT$10M+ Saved in Prevented Downtime Costs
- 👥 40%+ Reduction in Inspection Labor Costs
- ⚙️ 30% Extension in Automation Component Lifespan
