You probably know the Nokia brand from the early mobile phones market, don’t you (Nokia 3310)? Well, the company has evolved, focusing on bigger things in industrial digitisation.
Digital twins offer powerful tools to improve the maintenance of South Africa’s water treatment plants. However, their success depends on skilled human expertise to interpret and act on the data, experts from Stellenbosch University point out.
You probably know the Nokia brand from the early mobile phones market, don’t you (Nokia 3310)? Well, the company has evolved, focusing on bigger things in industrial digitisation.
In mining operations, a key cause of unplanned maintenance is the lack of collaboration between operations and maintenance teams when managing equipment throughout its lifespan.
You probably know the Nokia brand from the early mobile phones market, don’t you (Nokia 3310)? Well, the company has evolved, focusing on bigger things in industrial digitisation.
In Africa’s heavy industry — mining being no exception — the effects of power quality challenges are often benign and go unnoticed. Typically, plant managers focus on visible problems, which are merely symptoms rather than the root cause.
On the African continent, industries are converting their DOL machinery to speed-controlled systems. Unfortunately, this transition is triggering unintended spikes in harmonic distortions
RideOnTrack’s interoperable and customisable platform unifies voice communications across legacy and modern systems. After witnessing the efficiency the solution has brought to the rail transport sector, the company sees machinery maintenance teams in Africa’s heavy industry also benefiting.
In the Distributed Control System (DCS), ABB Process Automation has unlocked the perfect solution to address the challenge of the non-interoperability of equipment in process industries, a common headache for plant managers.
Just when we thought remote equipment monitoring on its own was the pinnacle of innovation in predictive machinery maintenance, a layer of machine learning is added to it. Ramjack’s Katrina Wertheimtouts machine learning to have a profound impact in simplifying complicated machinery maintenance tasks in African industries — of course, if embraced and applied properly
Using cheaper alternatives, mixing products, or applying one type across multiple applications may appear attractive at face value. But the Southern African Institute of Tribology (SAIT) warns that such decisions come with a high price to pay.
There may be advances in lubrication technologies, but basic practices in the application of grease remain as relevant as ever — in fact, more relevant than ever with escalating operating costs.