The phased cultivation of smart factories for 2025 has officially kicked off! How can industrial equipment maintenance and operations achieve "tiered empowerment"?

Recently, six departments including the Ministry of Industry and Information Technology launched the 2025 Intelligent Factory Tiered Cultivation Initiative (covering Basic, Advanced, Excellence, and Leading levels), clearly emphasizing the use of scenarios such as "interconnected production equipment," "equipment operation monitoring," and "intelligent maintenance services" to drive the intelligent transformation of manufacturing. As practitioners in industrial equipment operation and maintenance technology, we are particularly interested in: What kind of equipment operation and maintenance capabilities are required to support smart factories at different tiers? And how can businesses be empowered in a phased approach?

Level-4 Gradation: Advancing from "Functional" to "Leading"-Edge Capabilities

Basic Level: Addressing the "Whether or Not" Challenge—Driving digital transformation of equipment to enable data collection and connectivity.

Advanced level: Addressing "Can it be used?"—by leveraging data-driven approaches, shifting operations and maintenance from experience-based decision-making to informed, data-informed choices.

Elite Level: Addressing "Ease of Use"—by integrating AI technology to enable intelligent management throughout the device's entire lifecycle;

Leading-edge: Addressing "Can We Lead?"—exploring future models and driving intelligent transformation across all stages.

One of the key overarching themes running through Level 4 is "intelligent equipment operation and maintenance"—every step, from basic data collection to advanced predictive decision-making, requires robust technological support.

The four-level gradient adaptation capability of equipment operation and maintenance technology

Leveraging core scenarios such as "Equipment Operation Monitoring," "Fault Diagnosis and Prediction," and "Remote O&M Services" from the "Reference Guide for Typical Smart Manufacturing Scenarios (2025 Edition)," our technological capabilities can precisely align with the needs of enterprises at different stages of development:

Basic Level: Enable Devices to "Speak Up"

Many enterprises face the pain point of "equipment running, but status relies on experience" due to outdated equipment, protocol incompatibility, and data silos.

Smart sensing technologies—featuring vibration and temperature sensors—and industrial protocol conversion solutions (compatible with over 30 protocols)—are becoming critical, enabling seamless device data integration within just one week. This allows real-time collection of operational parameters such as rotational speed and energy consumption, while presenting a visualized "health profile" of the equipment. Additionally, it delivers anomaly alerts within seconds, paving the way for rapid, foundational-level "digitalization adoption."

Advanced Level: Letting Data "Drive Decisions"

Enterprises need to shift from "data collection" to "data-driven approaches" to address the issues of "frequent unplanned downtime and high operational maintenance costs."

The combination of mechanistic models (for analyzing device physical characteristics) and AI algorithms (such as transfer learning and pattern recognition) has become the core capability—enabling proactive early warnings of potential issues like bearing wear and motor aging, generating "predictive maintenance work orders," reducing unplanned downtime rates, and supporting the development of advanced "networked collaboration" as a benchmark initiative.

Excellence Class & Leadership Class: Empowering Technology to "Intelligently Transform the Future"

The Excellence and Leadership tiers need to explore "intelligent upgrades" and "future-oriented models," while equipment operation and maintenance technologies must be deeply integrated with AI large models and digital twins. By leveraging these advanced tools, enterprises can achieve fault diagnosis through large-model analysis of defect descriptions (with a response time of within 5 minutes), enable AR-based remote guidance (reducing the need for on-site travel), and simulate maintenance outcomes using digital twins (to validate the feasibility of "unmanned operations"). This approach will help companies transition seamlessly from "localized intelligence" to "holistic intelligent transformation."