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Deep Lube Intelligent Lubrication Technology

Our intelligent lubrication technology features a closed-loop control logic: Smart Sensing → Dynamic Decision-Making → Precise Execution.

1. Real-time collection of equipment operating parameters using integrated multi-dimensional sensors (oil pressure, temperature, vibration, etc.);

2. Deep analysis of historical and real-time data via neural network algorithms to build an equipment health assessment model;

3. Dynamic adjustment of lubrication cycles and dosages based on model outputs, enabling adaptive optimization of the lubrication strategy.

Traditional manual lubrication poses significant challenges: frequent equipment climbing leads to high labor intensity and safety risks. Lubrication cycles and dosages based on experience often result in under- or over-lubrication, accelerating equipment wear. In harsh conditions (e.g., high temperature, high dust), timely manual lubrication is difficult, further reducing equipment lifespan. With the growing need for smart oilfield transformation, developing intelligent lubrication systems featuring self-adaptation, real-time monitoring, and precise lubrication has become essential to overcoming these pain points.

Multi-dimensional sensing

• High-precision vibration spectrum analysis – real-time detection of bearing wear, gear misalignment, and other hidden faults
• Temperature-pressure dual monitoring – 0.1℃ sensitivity for overheating alerts, second-level response to pressure changes
• AI-driven grease condition diagnosis – intelligent grease life assessment via viscosity sensing and historical data modeling

Millimeter-precision lubrication

Full-scenario smart operation and maintenance

• Extreme environment adaptability – reliable operation from -40℃ to 70℃
• Cloud-edge collaboration – autonomous on-device decision-making + central brain continuous learning, with millions-scale model iteration
• Digital twin visualization – 3D equipment health maps, risk point visibility on one screen

Cost saving

According to a case study at an oil production plant, the annual maintenance cost per device decreased by 60%. Manual intervention frequency dropped from 8 to 2 times per month, well downtime decreased by 75%, lubricating oil consumption fell by 20%, resulting in annual grease cost savings of approximately 12,000 RMB.

Production efficiency improvement

The equipment's MTBF has increased from 1,200 to 1,800 hours, resulting in an annual incremental oil output of 60 tons. At current oil prices, this translates to approximately 240,000 RMB in additional annual economic benefits.

Safety risk control

It eliminates the need for high-altitude work, reducing workplace safety incidents by an estimated 80% and preventing equipment downtime losses caused by lubrication failure.