Smart O&M and Lubrication Management

1. Industry Pain Point

Traditional fluid monitoring data is isolated, unable to support predictive maintenance decisions.

2. Hangfeng Smart Ecosystem Solution

Beijing Hangfeng has built an integrated hardware-software closed-loop solution in the field of smart O&M and lubrication management. Through multi-parameter sensing, edge intelligence, and industrial AI algorithms, it enables synergistic monitoring of equipment health and lubricant performance, facilitating proactive maintenance. The core of the solution lies in integrating fluid data into full equipment lifecycle management, overcoming the traditional O&M pain point of "mechanical monitoring being separated from lubrication management."

3. Technical Architecture

4. Full-Parameter Real-Time Fluid Monitoring

5. Fluid Health Diagnosis AI Models

• Life Prediction Model: Based on fluid physicochemical parameters + equipment operating conditions (load/temperature), predicts Remaining Useful Life (RUL), dynamically adjusting oil change intervals.

• Case: Mining truck engine oil change interval extended from 500 hours to 800 hours, saving 18% annual lubrication costs.

• Wear Root Cause Analysis: Integrates vibration spectra (e.g., bearing fault frequencies) with particle shape features (cutting chips vs. spalling chips) to locate wear components.

• Example: Detection of 15-25μm fatigue particles + gear meshing frequency sidebands in gearbox → Diagnosis of tooth flank pitting.*

6. Smart Lubrication Execution

· Automatic Lubrication System Linkage: Triggers quantitative lubrication pump replenishment when bearing lubrication deficiency is detected (temperature ↑ + viscosity ↓).

· Oil Mixing Warning: Automatically locks the filling port upon sudden dielectric constant changes, preventing mixing of different lubricant brands.

7. Core Capabilities of Smart O&M

7.1. Multi-dimensional Fault Diagnosis Closed Loop

· Improved Diagnostic Accuracy: Fluid data reduces false alarm rates by 60% (e.g., vibration anomaly alone might be misdiagnosed as a balance issue).

7.2. Predictive Maintenance Drive

· Dynamic Maintenance Plan: Automatically adjusts inspection frequency based on Equipment Health Index (EHI), prioritizing resources for high-risk equipment.

· Spare Parts Collaborative Management:

7.3. Energy Efficiency-Reliability Balance Optimization

· Lubrication Benefit Maximization Model: Optimizes between "over-lubrication (energy consumption ↑) vs. under-lubrication (wear ↑)", reducing equipment friction power consumption by 8-12%.

· Oil Change Economy Analysis: Calculates the optimal oil change timing (considering oil cost + equipment downtime loss + waste oil disposal cost).

8. Solution Technological Advantages

9. Typical Application Scenarios

9.1. Wind Turbine Gearbox Health Management

· Challenge: Traditional oil analysis struggles to detect micro-pitting promptly.

· Solution: Online sensors monitor viscosity + moisture + particles. Edge computing analyzes vibration high-frequency envelope spectra.

· Value: Warns of tooth flank damage 6 weeks in advance, preventing tower collapse accidents (single incident loss prevention exceeds 2 million RMB).

9.2. Mining Equipment Lubrication Optimization

· Challenge: Dusty environment accelerates lubricant degradation.

· Solution: Real-time online monitoring of lubricant viscosity + moisture + mechanical wear debris. AI dynamically adjusts lubrication quantity (dust concentration ↑ → lubrication frequency ↑).

· Value: Bearing life extended by 30%; Annual maintenance cost savings of 500,000 RMB per unit.

9.3. Marine Main Engine System

· Challenge: High-sulfur fuel causes rapid decrease in cylinder oil base number (TBN).

· Solution: Online monitoring of cylinder oil viscosity + TBN. Predicts remaining effective mileage based on load changes.

· Value: Accurately plans port oil changes, reducing unscheduled downtime.

10. Platform Support Capabilities

10.1 Hangfeng Industrial Cloud (HF Cloud):

· Fluid Health Dashboard: Visualizes key fluid parameter trends and equipment correlations.

· Lubrication Knowledge Base: Automatically matches fault modes with handling recommendations (e.g., moisture exceedance → check cooler).

· Carbon Footprint Tracking: Statistics on lubricant consumption and waste oil disposal carbon emissions in the lubrication process.

10.2 Summary: Core Solution Value

Beijing Hangfeng achieves the following through its trinity architecture of "Fluid Sensing Network + Equipment Condition Chain + AI Decision Brain":

· Zero Unplanned Downtime: Fusion diagnostics provide 4-8 weeks advance warning of mechanical-lubrication compound faults.

· Lean Lubrication Costs: Oil change on demand reduces lubricant consumption by 20%; Waste oil disposal costs reduced by 50%.

· Leap in O&M Efficiency: Automated work orders shorten MTTR (Mean Time To Repair) by 60%.

Technology Moat: The industry-unique "Wear Debris - Vibration Signature - Temperature Field" 3D mapping algorithm and edge diagnostic module constitute the solution's core competitive advantage.