The coal mining industry is a typical asset-intensive industry. The continuous, stable, and efficient operation of its core production equipment (such as coal miners, scraper conveyors, roadheaders, main fans, hoists, crushers, etc.) is key to ensuring safe production and improving economic efficiency.
The traditional reactive maintenance and scheduled maintenance models have the following pain points:
1) Large losses due to unplanned downtime
Sudden failures of critical equipment cause production interruptions, resulting in huge losses in output and economic value.
2) High maintenance costs
Scheduled maintenance may lead to "over-maintenance," wasting spare parts and labor; while sudden failures may cause "under-maintenance," leading to secondary damage and high repair costs.
3) High safety risks
Undetected latent equipment faults may trigger serious electromechanical or even safety accidents.
4) Lagging equipment management
Relying on experienced workers without data support results in insufficient scientific decision-making.

1) Project Background
Company Name: Shanxi Lu'an Mining (Group) Co., Ltd. Gucheng Coal Mine
Typical Application Equipment: Main Shaft Hoist (Double Drum Friction Hoist)
Equipment Value: Over 50 million RMB
2) Solution
Priority monitoring of the most critical and expensive parts of the hoist.
a. Main Drive Motor
Monitor vibration, temperature, and current harmonics at the drive end; analyze harmonics to diagnose rotor bars and stator coil issues.
b. Gearbox
Install integrated temperature, vibration, and acceleration sensors on bearing housings and the exterior of the gearbox at the high-speed shaft, intermediate shaft, and low-speed shaft.
c. Main Shaft Bearings
Install integrated temperature, vibration, and acceleration sensors on the bearing housings at both ends of the main shaft.
3) Success Case: A Typical Predictive Maintenance Interception
Time: August 2023
Process:
a. Early Warning (6 weeks in advance)
The PHM platform issued an early warning notification indicating the health index of the high-speed shaft output bearing in the gearbox dropped to 85% (yellow alarm), although the overall vibration value was not exceeded. The platform recommended the fault cause as "early damage to the bearing outer ring."
b. Trend Confirmation
The maintenance engineer clicked the alarm and reviewed the trend and analysis charts. The system-generated spectrum showed clear energy peaks at the bearing outer ring fault characteristic frequency (BPFO) and its harmonics, with a slow upward trend in peak energy.
c. Decision and Planning
The maintenance team did not stop the machine immediately but continued to monitor the trend. The platform continuously updated the prediction, showing the remaining useful life (RUL) of the bearing to be about 35 days. The team pre-ordered spare parts accordingly and scheduled the repair during the routine maintenance window 35 days later.
d. Verification and Execution (1 week in advance)
One week before the planned shutdown, the system alarm escalated to a red high-risk alert, with the health index dropping to 60%. The team stopped the machine as planned and opened the gearbox for inspection.

Result:
1) Accurate Prediction
On-site disassembly revealed obvious fatigue spalling (pitting) on the bearing outer ring, fully consistent with the system diagnosis.
2) Avoided Unplanned Downtime
The repair was perfectly scheduled within the planned maintenance window, causing no unplanned production interruptions.
3) Cost Savings
Due to the very early warning, damage spreading to gears and shafts was avoided. The repair cost was limited to one bearing and 4 hours of labor. If the fault had occurred suddenly, it could have caused gear damage with losses exceeding 500,000 RMB.