As global demand for new energy batteries reaches the TWh scale, gigafactories face unprecedented challenges in operations and maintenance (O&M). With rapid expansion of production capacity, traditional maintenance strategies can no longer balance the critical trade-offs between production efficiency, cost control, and equipment reliability.

The LEAD PHM System addresses this challenge. Built on industrial big data and AI algorithms, it establishes a new benchmark for intelligent O&M in battery production.

The O&M Bottleneck in Gigafactories

O&M in battery gigafactories is often constrained by conflicts between production demands and maintenance execution.

• High Cost of Downtime: In a 20 GWh facility with six lines, a single unexpected equipment failure can cause production losses of more than €25,500 per hour per line.

• Unpredictable Failure Distribution: Equipment risk points are highly dispersed and difficult to forecast.

• Staffing Imbalance: Limited maintenance personnel frequently delay fault response times.

• Pressure on Frontline Teams: Frequent component failures create ongoing operational pressure, while spare parts supply chain delays further restrict repair efficiency.

• Complex Diagnostics: Faults requiring cross-department collaboration extend resolution cycles, directly threatening production stability.

These limitations demonstrate the need for a predictive, data-driven O&M model.

System Solution: Data-AI Integrated Predictive Maintenance

The LEAD PHM System introduces a lightweight, cost-efficient native architecture for predictive maintenance. By monitoring equipment operation data and applying AI-based risk assessment, it prevents unplanned stoppages and reduces unnecessary preventive maintenance.

Key Capabilities:

• Easy Deployment: Compatible with existing industrial PCs or centralized servers, requiring no hardware modification.

• Seamless Data Integration: Fully supports multi-brand monitoring systems and direct PLC data collection without sensor replacement.

• Broad Monitoring Range: Covers all core motion components, including servomotors, ball screws, bearings, sliders, and cylinders.

Core Functional Capabilities: From Data to Execution

The LEAD PHM System is structured around a “data → decision → execution” framework that directly addresses gigafactory maintenance challenges.

• Standardized Full-Process O&M: Built on 25 years of operational data accumulation, the system establishes integrated, standardized maintenance databases to enable consistent and digitalized management.

• Real-Time Predictive Alerts: Online monitoring of equipment status across the production line, enhanced by dual-track (data + algorithm) analysis, increases fault prediction accuracy and timeliness.

• Closed-Loop Maintenance Execution: A comprehensive fault knowledge base, SOP workflows, and spare parts management platform enable seamless transitions from fault detection to spare parts procurement and maintenance completion.

Industrial Validation: Proven Capacity and Cost Benefits

The LEAD PHM System has been validated across multiple large-scale enterprise production lines. Results from mass deployment include:

• Fault Coverage: >50% across entire plants

• Prediction Accuracy: >85%

• Downtime Reduction: >159 hours prevented within six months

• Economic Impact: Delivering annual production capacity economic benefits equivalent to hundreds of thousands of euros through improved production continuity.

Industry Impact and Transformation

The system supports a shift from the traditional “emergency repair + scheduled maintenance” model to a proactive “improvement repair + AI predictive maintenance” approach.

By enabling precise fault forecasting and preemptive intervention, the LEAD PHM System:

• Enhances Overall Equipment Effectiveness (OEE)

• Improves product yield and quality stability

• Optimizes spare parts and resource utilization

• Establishes a new benchmark for intelligent maintenance in power battery manufacturing

Conclusion

The LEAD PHM System demonstrates that predictive, AI-driven O&M strategies are essential for the sustainable operation of gigafactories. By integrating real-time monitoring, predictive analytics, and closed-loop execution, it ensures production stability, reduces downtime, and delivers significant economic benefits measured in hundreds of thousands of euros annually.

As the industry continues to scale, predictive maintenance systems such as LEAD PHM will play a central role in enabling resilient, efficient, and intelligent Gigafactory operations.