How Predictive Maintenance is Reshaping Manufacturing Competitiveness

GE Vernova Expert Points to AI and Digital Twin-Driven "Prescriptive Maintenance" as the Next Frontier for the Industrial Internet

(Commentary) – Global manufacturing stands at a critical crossroads. On one side are increasingly complex production equipment, high operational costs, and fierce market competition. On the other is a historic opportunity brought by technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and Digital Twins. In this transformation, Predictive Maintenance (PdM) is no longer an option but a strategic necessity.

At a recent industry summit, Mr. YU Siyuan, Solution Architecture Director, Asia Pacific at GE Vernova, outlined a clear vision for the future of industrial operations. He pointed out that the evolution of maintenance models can be divided into four stages:

1. Corrective Maintenance: Fix it after it breaks. Costly.

2. Preventive Maintenance: Maintain on a schedule, regardless of condition. Risks over- or under-maintenance.

3. Predictive Maintenance: Based on the actual condition of the asset, providing early warnings for precise intervention.

4. Prescriptive Maintenance: Not only predicts failures but also autonomously analyzes root causes and recommends or even automatically executes the optimal decision.

"The industry is moving from 'predictive' to 'prescriptive'," asserted Mr. Yu, "and the engine driving this change is industrial AI platforms like Proficy CSense."

Beyond the Alert: The Strategic Value of PdM
The traditional value of PdM lies in avoiding unplanned downtime and reducing repair costs. But its strategic significance goes far beyond. It is increasingly becoming the data-driven decision-making hub for enterprises.

Through continuous asset health monitoring, the accumulated asset performance data becomes an invaluable digital asset. This data can be used to:

• Optimize Supply Chains: Accurately predict major equipment overhaul times, facilitating advanced parts preparation and reducing inventory costs.

• Improve Product Design: Analyze equipment failure modes to provide feedback for the reliability design of next-generation products.

• Innovate Business Models: Shift from "selling equipment" to "selling services," such as offering guaranteed uptime contracts billed per operating hour, extending core competitiveness from manufacturing to services.

Technology Convergence: Cloud-Edge Synergy and Digital Twins Form the New Foundation
Mr. Yu emphasized Proficy CSense's "up to the cloud, down to the edge" future strategy, which reflects two major industry tech trends: Cloud-Edge Synergy and Digital Twins.

"Up to the cloud" refers to leveraging the unlimited computing power of the cloud for deep mining of historical data, training complex models, and plant-wide energy management. "Down to the edge" means deploying lightweight analytical models at the edge to meet the stringent requirements of industrial sites for real-time performance, reliability, and data security.

Digital Twins are the bridge connecting cloud and edge, the physical and digital worlds. A high-fidelity digital twin of an asset can not only mirror its state in real-time but also use platforms like CSense for simulation and inference. For example, before executing a repair plan, it can be verified in the digital world first, ensuring success. This marks the transition of maintenance from "accurate prediction" to "intelligent decision-making."

The Future Challenge: Organizational Change is Harder Than Technology
Although the technology is ready, full-scale deployment still faces challenges. The biggest obstacle is often not technical but human mindset and organizational processes.

Predictive maintenance requires breaking down departmental silos and achieving the deep integration of IT (Information Technology) and OT (Operational Technology). It changes the job responsibilities of maintenance personnel from hands-on repair to data analysis and insight interpretation. Companies need to invest in new skills training for employees and adjust performance evaluation systems to encourage proactive behavior based on predictions, rather than rewarding "firefighters."

Conclusion: A Key Lever for Winning the New Industrial Era
Globally, competition in high-end manufacturing is intensifying. Cost advantages are giving way to efficiency, quality, and reliability advantages. Predictive maintenance, as one of the Industrial Internet applications that most directly generates economic benefits, is a key lever for seizing these new advantages.

For Chinese manufacturing, embracing predictive maintenance is no longer a question of "if" but "how fast" and "how deep." Those who can first transform data into insight, insight into action, and action into profit will inevitably occupy the high ground in the new industrial revolution. The solutions brought by companies like GE Vernova provide a roadmap to the future, but the real journey requires each manufacturer to undertake with its own determination and wisdom.

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