The petroleum and petrochemical industry has large-scale equipment, continuous operations, and harsh operating environments. Sudden shutdowns can cause huge economic losses and safety risks. Traditional maintenance relies on regular inspections and manual patrols, which suffer from low efficiency, high misjudgment rates, and high costs.
Predictive maintenance uses sensors, IoT, big data, and AI models to monitor the status of key equipment in real time, identifying potential issues such as bearing wear, rotor imbalance, and pipeline leaks in advance, shifting from "reactive repair" to "proactive prevention." Its value lies in reducing unplanned downtime, lowering operation and maintenance costs, and improving safety and environmental protection levels. It is a crucial approach for digital transformation and smart factory construction in petroleum and petrochemical enterprises.

Production continuity is strong; once key units shut down, it will lead to huge economic losses and supply chain disruptions. Equipment types are diverse and operating conditions complex, with high temperature, high pressure, and corrosive environments making hidden dangers difficult to detect through manual inspections. On-site data is scattered across different systems, leading to incomplete collection and uneven quality, making effective diagnosis difficult. Traditional maintenance relies on manual experience and regular inspections, prone to over-maintenance or missed inspections, increasing costs and embedding safety and environmental risks. Under the background of "dual carbon" goals and intelligent manufacturing, enterprises have an increasingly urgent demand for more efficient and accurate predictive maintenance. Shutdown losses are huge: production continuity is strong, and unplanned downtime caused by key equipment failure results in massive economic losses.

Core pain points:
1) Complex and diverse equipment
Includes various types of equipment such as compressors, pumps, and heat exchangers, with harsh operating conditions and hidden dangers that are difficult to detect in time.
2) Harsh operating environment
High temperature, high pressure, and corrosive environments accelerate equipment aging, making manual inspections difficult to cover.
3) Dispersed and isolated data
Monitoring data is stored in different systems with uneven quality, making it difficult to form a unified diagnostic basis.
4) Reliance on manual experience
Traditional maintenance mainly depends on regular inspections and manual judgments, prone to over-maintenance or missed inspections.
5) Safety and environmental risks
Equipment failures may cause fires, explosions, or leaks, bringing significant safety and environmental hazards.

1) Enhance Production Continuity
Provide early warnings for equipment hazards, reduce unplanned downtime, and ensure stable operation of key units.
2) Reduce Operation and Maintenance Costs
Optimize maintenance strategies, reduce over-maintenance and spare parts waste, and lower manual inspection intensity.
3) Enhance Safety and Environmental Protection
Timely identify risk sources, reduce the probability of accidents and leaks, and support compliance and green production.
4) Extend Equipment Lifespan
Improve equipment lifecycle utilization through precise diagnostics and reasonable maintenance.
5) Data-Driven Management
Accumulate operation and maintenance big data to support enterprise decision-making and smart factory construction.

In the process of advancing the construction of an intelligent factory, a chemical company introduced a predictive maintenance system in 2021, with an overall solution provided by Zhongyun Technology. The project covers the main production equipment in the plant, installing over 400 monitoring points, including more than 100 core pieces of equipment such as compressors, circulation pumps, and blowers.
After the system went online, through real-time data collection and fault algorithm analysis, it identified more than a hundred potential hazards such as bearing wear, coupling loosening, and rotor imbalance, successfully avoiding unplanned shutdowns and production accidents while maintaining zero missed reports. More importantly, the company's inspection model was optimized, reducing manual inspection needs by over 30%. Routine maintenance shifted from "passive response" to "active prevention," significantly improving equipment operational reliability and production safety.