Effective maintenance is key to ensuring the long-term stable operation of folding machines. While the maintenance of two-fold machines is relatively straightforward, a systematic maintenance plan remains essential. Daily maintenance includes cleaning guide rails and transmission components, checking air source filters and lubricators, and verifying the functionality of safety devices. Preventive maintenance focuses on regularly replacing wear-prone parts such as seals, air tubes, and filters to avoid unexpected failures. Although maintenance records are simple, they are crucial for tracing equipment status and planning spare parts inventory.

Maintaining three-fold machines requires more specialized knowledge and tools. Regular calibration is critical to ensuring precision, including zero-point calibration of servo systems, sensor calibration, and adjustment of mechanical clearances. Preventive maintenance strategies are based on operational data: monitoring anomalies in servo motor current, tracking wear trends of key components, and analyzing fault history to predict weak points. Maintenance plans are coordinated with production schedules, utilizing production gaps for upkeep to minimize downtime.

Maintaining four-fold machines is a highly specialized task that requires interdisciplinary knowledge. Skills in precision mechanical adjustments, servo system tuning, electrical control optimization, and software parameter configuration are all essential. Advanced four-fold machines are equipped with comprehensive self-diagnostic systems capable of identifying potential issues and providing solutions. Predictive maintenance relies on big data analysis: by monitoring multi-dimensional data such as vibration, temperature, and current, the remaining lifespan of components is predicted, and replacements are scheduled at the optimal time.

The evolution of preventive maintenance strategies reflects advancements in technology. From periodic maintenance (time-based) to condition-based maintenance (equipment state-based) and further to predictive maintenance (data analysis-based), maintenance efficiency and equipment utilization have continuously improved. IoT technology enables remote maintenance, allowing experts to diagnose equipment issues via network connections, guide on-site personnel, or directly adjust parameters.

Capacity building for maintenance teams is equally important. Systematic training programs ensure that maintenance personnel acquire the necessary skills; knowledge management systems accumulate fault cases and solutions; and long-term partnerships with equipment suppliers provide ongoing technical support. As equipment becomes more intelligent, the role of maintenance personnel is also evolving: transitioning from mechanical repairs to data analysis, system optimization, and technical management.