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In most industries, upkeep involves waiting. Issues are resolved once they occur. However, in the 21st century, an era marked by data and automation, this method is no longer logical. The answer might be predictive maintenance. This strategy employs sensors and software to monitor machinery performance instantly and forecast potential failures.

Edward Khomotso Nkadimeng, an instructor and scholar specializing in artificial intelligence and data systems within the field of nuclear and particle physics at Stellenbosch University, hasresearchedhow a predictive maintenance model can ensure the continuous operation of essential systems – ranging from research equipment to national infrastructure. He discusses why this method could serve as an effective tool for resilience throughout Africa.

What is a predictive maintenance model and what was the reason for developing it?

For many decades following the worldwide industrializationboom, many industries followed a basic approach: wait for a machine to fail, then fix it. This made sense when machines were less complex and downtime was considered a normal part of operations.

Regular upkeep is also widespread, yet it remains ineffective and typically relies on time intervals rather thanactual machine condition. This method requires time, money, and occasionally compromises safety. Modern systems are moreinterconnected and expensive to halt.

A forecasting maintenance model is a data-based system that predicts machinery breakdowns ahead of time.it happens. It anticipates when systems are deteriorating, instead of merely responding. It keeps track of numerous systems, ranging from industrial pumps, compressors, and turbines to scientific equipment, by gathering live data such as vibration (which indicates how much a machine physically moves), temperature, pressure, and voltage.

These readings are obtained from Internet of Things (IoT) or condition-monitoring sensors. Even devices that are not highly advanced can be equipped with these tools to generate such information. After being gathered, the data is used by machine learning models that are trained to identify patterns linked to gradual deterioration leading to malfunction.

The model keeps track of various systems: industrial pumps, compressors, turbines, and high-precision scientific equipment (cyclotrons, vacuum pumps, beamline diagnostics). It is intended for systems that generate sensor datacan be collected– any device that produces detectable signals. It relies on real-time data from vibrations, the physical movement of a machine part, where minor changes in vibration strength or frequency often indicate upcoming mechanical issues, such asbearing wear or rotor misalignment, as well as temperature, pressure, and electrical voltages.

Although sophisticated equipment can generate more detailed information, older devices can also gain advantages through the addition of sensors. The approach is therefore widelyapplicableto detect when they are gradually moving towards defeat.

At NRF-iThemba LABS, a South African national nuclear and accelerator research facility, along with Stellenbosch University, I developed a system in response to need. Our team consists of physicists, engineers, and computer scientists working together on high-precision experiments in nuclear and particle physics.

The research tools are intricate, costly, and frequently unique. When they malfunction unexpectedly, experiments halt, data is lost, and public money is wasted. For instance, we collaborate with a 70 MeV device.cyclotronsfor the production of isotopes, superconducting magnets,radiofrequency acceleration cavitiesand vacuum systems. These are unique devices, vulnerable to interruptions.

Therefore, the objective was to develop a cost-effective, self-training system capable of expanding from our research equipment to the industrial infrastructure that supports African economies through pumps, turbines, and power grids. Comparable predictive maintenance systems are utilized in industrial power stations, water supply companies, and aviation, helping to minimize…unplanned downtime by 20%-40%. Our version tailored for African laboratories and industrial systems employs affordable Internet of Things sensors along with cloud-powered AI.

What did you gain from the model? How does this benefit you?

The first lesson I gained is that machines murmur before they shout. Long before a failure occurs, they exhibit minor indicators such as slight tremors, minor voltage decreases, or gentle variations in speed.

Having sufficient data regarding vibration, temperature, pressure, voltage, and motor load, for instance, these data flows serve as input for AI models. These patterns create a type of language, with artificial intelligence acting as the interpreter.

By training the model using actual operational data such as pump vibration over time and other measurements, we found that failures are not random—they exhibit identifiable patterns. Once the system recognizes these patterns, it can forecast upcoming issues and even recommend appropriate actions. The main advantage lies in timing—scheduling maintenance precisely when required, neither too soon, which leads to unnecessary use of parts and labor, nor too late, which could result in major breakdowns.

Rather than excessively maintaining equipment or waiting for it to break down, maintenance can be performed precisely when required. This approach conserves resources, minimizes interruptions, and ensures operations continue efficiently. Since this concept is applicable universally, it is equally effective in factories, hospitals, and water infrastructure as it is in research laboratories. For instance, identifying a deteriorating motor before a production line stops in a manufacturing facility, or using ventilator sensors to anticipate pump failure in a hospital, or overseeing municipal pumps.to prevent water shortages.

What are the real-world effects of using the model?

The real-world effect is significant. Predictive systems prevent outages, water shortages, and unexpected shutdowns—problems that influence everyday life and critical services. A clear example is seen in South Africa’s power cuts: the transformers of the power company Eskom are monitored for predictive purposes.faults. In Cape Town, forecasting maintenance of water systemsreduces pump downtime. They also contribute to safer work environments and more effective financial planning.

In African countries, particularly, where technical resources are frequently limited, predictive maintenance serves as a means of resilience. It shifts from reactive problem-solving to proactive planning. By utilizing cost-effective IoT sensors (small devices that gather data such as temperature), cloud-based AI (online software that analyzes this data in real-time), andself-learning algorithms, upkeep turns into an ongoing, self-operating, and intelligent process.

It’s the unobtrusive aspect of AI, ensuring that lights remain on, pumps function, and the economy stays steady. Physics, data, and engineering can subtly collaborate to maintain critical systems and ensure their dependability.

Edward Khomotso Nkadimeng, Postdoctoral Fellow: Artificial Intelligence and Data Systems in Nuclear and Particle Physics, Stellenbosch University

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