Asset failures are costly and stressful. This is particularly true for mining operations, where a single hour of downtime can result in millions of dollars of lost revenue.
Bachoir elaborates on the prescriptive maintenance approach with the help
of an example. “If you have vibration monitoring sensors set up on a rotating equipment, you can detect when the vibrations escalate beyond set limits. In all these cases the data need to be further interpreted to identify the real problem. Vibration can be caused by several factors such as wear on the bearing, an impeller/rotor imbalance or a loose mounting bolt. Additionally, its cause and impacts can be seen upstream and downstream of the equipment in the process data; but this is not indicated with vibration sensors. In this case, predictive maintenance cannot tell you exactly what the problem is without further analysis and validation. By using multivariate analysis – which looks at mechanical and process variables – Aspen Mtell® looks at signatures that the human eye cannot see way before a sensor reading reaches its set limit and can tell you exactly what the incoming problem is. “With vibration monitoring alone, by the time the alarm sounds, the equipment may be already damaged and you may have a very short timeframe to repair or replace that equipment.” This precision in predicting failures is rooted in Aspen Mtell’s® smart algorithms. Aspen Mtell® is flexible and can integrate with most OEM and third-party condition monitoring systems, as well as with existing process and mechanical sensors. Once set up, the system uses software elements – known as agents – to identify signatures for both normal and failure behaviours. When any failure behaviour is detected, Aspen Mtell® agents send alerts to users, prescribing appropriate maintenance activity based on the machine’s historic behaviour. The operations and maintenance teams have an extended warning of the potential for failure and can make decisions to remediate the failure in the most cost-effective way or adjust production. “Moreover,” says Bachoir, “Each time a new anomaly is detected that is not historically recorded, the maintenance team can investigate the cause and categorise it either as a new normal behaviour or a new failure signature.” While in the traditional preventive maintenance approach it is quite common for mining companies to schedule regular maintenance sessions at fixed intervals, Bachoir says Aspen Mtell’s® prescriptive approach allows the maintenance teams to manage repair or replacement choices over the lifecycle of their assets. “Scheduled maintenance shutdowns are often planned based on the expected life of the equipment. But in practice the equipment’s useful life depends on so many other factors such as process variations or equipment defects.” “So in the preventive model, the equipment is still prone to breakdowns. On the other hand, stopping the machines at fixed intervals for regular maintenance schedules is also a waste of valuable time and resources, as in many cases the full life of the equipment is not utilised,” says Bachouir. The increased reliability with asset monitoring allows plant managers to use their resources more efficiently. “For critical operations, plant managers tend to have standby equipment in place to deploy in case of an emergency. Using Aspen Mtell® allows them to avoid such emergencies by improving the reliability of the existing equipment. Having the equipment run at optimal efficiency and energy consumption also results in higher overall yield.”
While some asset monitoring systems require expert skills and knowledge of industrial equipment, Bachoir says Aspen Mtell is far simpler to implement and offers faster payback. “Aspen Mtell is very user-friendly. It requires minimal input from the end user and is very fast to implement. After a quick initial set up, the asset owners are trained to fine-tune the system and improve the agents as they go along,” he explains. Combining the Aspen Mtell solution with the client’s domain knowledge is a key driver for success, as it minimizes any delay between a notification, investigation and remediation. “The beauty of Aspen Mtell’s algorithm is that once set up, it continues to learn and adapt to the system. This means more and more reliability and peace of mind for the asset owners.”
Having an efficient asset monitoring system also means more safety for the personnel, according to Bachoir.
Joint venture article with AspenTech and Australian Mining volume 112/5 | JUNE 2020