The challenge
Machine downtime lowers productivity
The minerals processing industry relies heavily on machinery and effective operation of equipment is critical to productivity. Downtime as a result of a machine upset can cost companies significant time, money and production losses.
We know that the sound a machine makes can be an indicator of its health and how well it’s performing. This is particularly true for minerals processing operators who rely on machinery to rapidly transport, feed and crush ores.
While some machine problems are immediately obvious to the human ear, others can be subtle and harder to detect.
This means that issues can go unnoticed for long periods and potentially become more serious, causing damage, drops in efficiency and ongoing production losses.
Our response
Keeping an ear out for machine faults
Using our expertise in wireless technologies, we developed an acoustics emissions analyser to lend an ear and passively monitor machine performance.
The device cuts through background noise generated by masses of heavy machinery, allowing operators to diagnose how their machinery is performing.
Faults can be detected rapidly so that as soon as machinery doesn't sound right, maintenance monitors can can be alerted to fix the problem. This results in less downtime which ultimately leads to cost savings.
The results
Rapid detection and response
The acoustic emissions analyser has been successfully installed by Xstrata Technology on their fine grinding mill at Ernest Henry mine in north Queensland.
The technology has been picking up sounds that indicate the distribution of grinding media and how efficiently the mill is working, allowing the mine operators to rapidly respond to changes in processes or machine condition. This has given Xstrata the ability to optimise processes, minimise downtime, maximise efficiency and save money.
The acoustics emission analyser has wide application in many industrial processes where high frequency noise (above the audible frequency range of humans) is influenced by process and machine condition.