Machinery Overview
The case involves a regrind mill located in an Australian mine. The mill is a secondary grinding unit that further reduces ore particle size after the primary grinding stage to ensure effective mineral liberation before downstream processing.
Its drivetrain consists of a motor, coupling, reducer, and pinion that engages a large girth gear to rotate the mill shell.
In this case, the focus is on the pinion shaft drive-end bearing, a critical element that supports the pinion under heavy load and harsh conditions, while enabling the pinion to transfer torque reliably to the girth gear.
Monitoring Devices and Software Set-up
I-care reliability engineers installed Wi-care™ vibration sensors across the drivetrain, including two on the motor, four on the gearbox (input and output shafts), two on the pinion shaft bearings, and two on the mill bearings.
Once set up, Wi-care sensors send machinery health data to I-see™, our AI-powered analytics platform. I-see™ categorises the data to determine whether the equipment is operating normally, showing early warning signs, or entering a critical alarm state.
The platform then compiles these insights into clear health reports, which are reviewed by I-care analysts. This expert review ensures that potential issues are identified quickly and that actionable recommendations are provided, as detailed in the following steps.
Detailed Analysis
Step 1 | Early Detection
On June 3rd, one week after the sensors were installed, I-see™ detected abnormal vibration patterns on the pinion shaft drive-end bearing.
Very high frequency envelope measurement (I-DNA) revealed unusual asynchronous vibrations with sidebands near 1× RPM, while the corresponding waveform showed impacts.
Together, these indicators pointed to a possible severe bearing defect.
Step 2 | Request for Technical Information
Following the alert, the I-care engineer requested further technical details to validate the initial finding and refine the diagnosis.
Although the vibration data suggested a bearing defect, asset-specific information was needed to confirm the source of the anomaly and rule out other possible causes. The site was therefore asked to:
- Provide a detailed frequency sheet and additional trend data to better characterise the defect and monitor its evolution.
- Confirm the exact bearing types of both the pinion and the mill, along with technical details on coupling types and gearbox tooth geometry, to complete the frequency sheet.
- Visually check, if feasible, the alignment and gear condition between the pinion and the girth gear, since misalignment or tooth wear could contribute to the observed vibration.
- Inspect, if feasible, the coupling and alignment between the gearbox output shaft and the bearing-supported shaft, and examine the coupling for possible wear or looseness.
- Stay alert for unusual noise from the coupling, which could be an additional indicator of mechanical stress or wear.
Step 3 | Refined Diagnosis
On June 6th, after receiving the requested technical information, the I-care analyst refined the diagnosis.
The pinion shaft speed was confirmed at 122 RPM (derived from 738 RPM × 22/133 teeth), which allowed the team to accurately link the anomaly to the Ball Pass Frequency Outer race (BPFO) of the driven-end bearing (SKF 22344 CCK/W33) at approximately 6.65 orders.
The presence of sidebands at 1× order around this fault frequency indicated modulation once per revolution, typically associated with high internal bearing clearance. This pattern strongly supported the diagnosis of a bearing defect.
The analyst also noted that as the defect progresses, the sharp vibration spikes would gradually diminish as noise levels increase due to wear. At this stage, however, the spikes remained very pronounced, indicating that the defect was still in an early phase.
It is worth noting that two independent service providers — one conducting an offline vibration analysis and another performing an infrared inspection — had failed to identify any issues. A third engineering company suspected that “a fault” might exist in the bearing but could not specify its exact nature.
Based on these findings, I-care advised continued close online and local monitoring, as the defect was still in its early stage and did not yet require immediate intervention. Bearing replacement was recommended at the next available maintenance window.
Step 4 | Monitoring
Over the next four weeks, the vibration patterns remained stable, with no significant changes.
Step 5 | Planned Intervention
On July 16th, six weeks after the issue was identified, a scheduled maintenance window provided the opportunity to perform an overhaul. The bearing was replaced during this shutdown, avoiding unnecessary risk of unplanned failure.
Step 6 | Post-Overhaul Verification
Measurements taken after the overhaul confirmed that the machine was in good condition.
No substantial bearing fault frequencies were detected, and no further anomalies were observed.
I-care continued to track the machine’s condition through ongoing online monitoring.
Results
When Predictive Maintenance Pays Off — Over $1 Million Saved with I-care
If the pinion bearing had failed unexpectedly, the mill could have been out of service for 12 to 24 hours. With a plant throughput of 220 t/h at a head grade of 2 g/t and the prevailing gold price, each hour of downtime equates to roughly $46,683 in lost metal value. An unplanned stoppage would therefore have resulted in production losses of between $561,000 and $1.12 million, excluding the additional costs of emergency repairs or potential damage to the girth gear.
By detecting the defect early, I-care enabled a planned bearing replacement during a scheduled shutdown, avoiding costly downtime and preventing collateral damage to critical components.
Importantly, while two independent service providers failed to identify the fault, and another engineering company could only confirm a generic issue without pinpointing the source, I-care’s system diagnosed the problem precisely within the first week of operation. Early detection with Wi-care™ sensors, I-see™ analytics, and expert follow-up turned a potential disruption into a controlled intervention, securing operational continuity and protecting the mine’s bottom line.
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