The widespread application of industrial internet is revolutionizing the operation and maintenance (O&M) of electromechanical equipment. Predictive maintenance, a data-based advanced O&M model, has moved from concept exploration to large-scale implementation, bringing significant benefits to industrial enterprises.

Predictive maintenance relies on a technical closed loop of data collection, transmission and analysis. Sensors for vibration, temperature and current deployed on key electromechanical equipment collect real-time operational data. This data is transmitted to cloud platforms via edge gateways and networks, and machine learning algorithms build equipment health models to accurately warn of potential faults like bearing wear and rotor imbalance.

Its great value lies in shifting from passive to active O&M. It avoids unplanned downtime caused by sudden faults to ensure production continuity. Meanwhile, maintenance changes from traditional scheduled to on-demand, reducing waste from over-maintenance and damage from under-maintenance, thus cutting costs and extending equipment service life.

For manufacturers, predictive maintenance should start with key equipment—prioritizing transformation of core, high-value production-line equipment to maximize ROI. Success hinges on partnering with industrial internet platforms with industry expertise, and focusing on daily data accumulation and continuous algorithm model optimization. This shift is not just a technical upgrade, but a major innovation in enterprise O&M management systems.