SKF has developed an innovative real-time monitoring solution to proactively learn about the condition, limits, and real performance of key mining equipment over time.
SKF has invented a real-time performance monitoring solution for bulk material handling equipment that uses internal sensors to scrutinise the performance of slow speed slew bearings and bucket wheel bearings.
SKF slew bearings product manager Lionel Martin said the solution is the result of thousands of hours of data monitoring and analysis of bearings. It now has the potential to replace inefficient and risky manual measurements.
“Manual data capturing takes place between one and three times a year, depending on the machines,” he told Australian Mining.
“(Manual monitoring) has multiple disadvantages. Firstly, it’s quite intrusive. You need to be as close as possible to the bearing to take measurements with manual tools, which can put staff at risk and introduce measurement errors based on human interaction and environmental challenges.”
Recent staff shortages and restricted site access due to health regulations have triggered a new approach towards performance monitoring.
Automating the monitoring means that not only can bearings be monitored around the clock, but companies are saving a significant amount of money and resources that they can re-deploy elsewhere.
The nature of manual monitoring, according to Martin, can also lead to incorrect conclusions which can create doubts around such results.
“Manual measurements aren’t very accurate; the repeatability and reproducibility are low, the manual measurement process is not stable,” he said. “You can have someone check the condition of your bearing and then three months later another person will tell you the bearing is in better condition than it was three months ago.”
To make measurements more consistent and repeatable, SKF launched a real-time monitoring solution for excavator slew bearings in 2021, with bearings coming pre-equipped with sensors. The Swedish company has since expanded the solution to a wider range of slow speed bearings, providing an even more accurate picture of the bearing’s performance.
Martin said the solution was one-of-a-kind.
“I have been in this industry long enough and we’ve done enough benchmarking to say that there’s nothing equivalent to this on the market right now,” he said.
“The performance monitoring solution can be connected to the machine control signals and generate more insight on how the bearing operates under specific machine operating conditions.”
Machine learning using sophisticated algorithms allows the data to be accurately interpreted and provide a clear indication of wear rates and machine health during operation.
“Monitoring the asset between one and three times a year, you will only get lagging indicators. You will never be able to proactively learn about the condition, limits, and real performance of your bearings over time.
Only online performance monitoring allows operators to understand a bearing’s true life and to make intelligent decisions around replacements.
In some situations, it might not be about avoiding asset failure, but rather ensuring a part isn’t replaced before the end of its working life.
This will save on inventory, cost of ownership and may even allow for remanufacturing of a bearing rather than buying a new one. It can even help to reduce the carbon footprint of mining companies.
Data from the condition-monitoring solution can also provide insights into operator behaviour. If an employee is found to be operating dangerously or inefficiently, the mining company can implement training measures to rectify any issues.
With the SKF system, proactive mining companies now have a solution to make smart and cost-effective decisions about maintenance cycles. Such insights are not accessible without the support of 24/7 connectivity using real-time data analytics.
This feature appeared in the June issue of Australian Mining.