Capital Equipment News September 2019

DIGITALISATION IN MINING

for this, a local display for the operator to reference can be used. This enables the operator to make an informed decision on criticality. Mobile equipment breakdown frequency will be reduced and reliability increased,” explains Zeelie. Every plant will have critical and non-critical equipment, says Umar. Online monitoring devices are usually used for critical equipment and hand-held data devices used when the equipment does not warrant expensive online devices. SKF’s Rotating Equipment Performance model however allows for both online and hand-held data acquisition devices to be deployed in the plant utilising OpEx instead of CapEx. This model bundles together condition monitoring services, remote diagnostics and other services and both lubrication and bearing

products into a unique tailor-made package to meet each client’s needs. Ntsele says where multiple equipment is concerned, harnessing the power of digital tools enhances the ability to gather insights that would otherwise be missed due to the inherent complexity of the interdependencies of systems. “Therefore, OEE is greatly impacted when an integrated digital approach that covers the four technology clusters I described earlier is adopted and implemented across the mining value chain, especially when ore parameters form part of the data matrix,” he says. Data analytics Data analytics play a significant role in the digitalisation process, says Zeelie. “It is the process of interpreting and understanding what is being measured. When done correctly, it will result in better understanding of the process and what the drawbacks are. The drawbacks can then be improved on or eliminated. The end goal of data analytics should be to use the available information to understand the process and improve on productivity and reliability,” explains Zeelie. Umar adds that SKF globally has embarked on the road to Big Data. There are nine certified Remote Diagnostic Centres, which means that equipment can be benchmarked against similar equipment anywhere in the world. These centres are working towards an uniformed methodology of data collection, data management, analysis and reporting. The information can be viewed via dashboards but it is also used for much larger projects such as Prognosis and Machine Learning. “The goal in the not too distant future will be for plant equipment to self-diagnose impending faults and predict lead times to failure. This will enable pieces of equipment themselves to order critical components for replacement early in the failure curve and help alleviate logistic issues on long lead times. The knock-on effect will be leaner stock holdings, just in time delivery and well-planned maintenance windows, all of which will have huge financial savings while increasing plant efficiency,” explains Umar. Ntsele says data analytics is essentially a process of analysing sets of data with the objective of gaining insights that will enable decision making. From a productivity perspective, and because of its timely nature – daily, weekly and monthly – KPIs can be based upon this data and thus guide or drive plant personnel behaviour to achieve the required result or output. According to Nellessen, today’s mining machines generate a large amount of machine data. The vast amount of data needs to be filtered, clustered and aggregated in such a way that it can be processed. “In combination with equipment knowledge the generated data can be interpreted in a meaningful way. Data patterns can be linked to specific events and machine behaviour, for example, detection of anomalies can prevent machine failures or provide the basis for optimised maintenance effort and optimised holding of spare and wear parts inventory. In other words, data analytics turns data into meaningful information,” concludes Nellessen. b

SKF says condition monitoring using vibration is the most common technology being used.

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