Reactive vs preventive vs predictive

Three maintenance strategies, and most SA mines run a mix weighted toward the first two:

  • Reactive — run to failure, then fix. Cheapest until the failure halts production, damages adjacent components, or hurts someone. On critical equipment it's the most expensive option of all.
  • Preventive — service on a fixed schedule. Better, but you either service too early (wasting good component life and labour) or too late (the asset fails before its slot). The calendar doesn't know the machine's actual condition.
  • Predictive — let the equipment's own data decide. Vibration, temperature and current trends reveal a developing bearing, gearbox, imbalance or alignment fault weeks ahead, so you act exactly when needed — no sooner, no later.

Despite rising interest, industry practice in South African mining remains largely reactive and preventive. That gap is precisely the opportunity: the assets are extreme-duty, the downtime is expensive, and the early-warning physics is well proven.

The economics in one line: on a critical asset, the question isn't "can we afford condition monitoring?" — it's "what does one unplanned stop of this machine cost us, and how many do we need to prevent to pay for watching it?" Usually the answer is: very few.

Predictive maintenance vs oil analysis — not either/or

South African mines already use condition monitoring through oil and fluid analysis — sampling lubricant for wear particles to spot trouble. It's valuable and well-established. But it's a periodic snapshot: you learn about the machine's health each time you sample, not in between. Real-time IoT condition monitoring fills that gap — continuous vibration, temperature and current watching the asset every minute, catching faults that develop between oil samples and alerting you the moment a trend turns. The two are complementary: keep your lab, and add live sensing so nothing develops unseen in the weeks between samples.

What to monitor, and how

  • Vibration — the workhorse. Bearings, imbalance, misalignment and looseness all announce themselves in the vibration signature long before failure on mills, crushers, conveyors, pumps, fans and gearboxes.
  • Temperature — bearings and windings running hot signal trouble; cheap to measure, easy to trend.
  • Motor current — a clamp-on sensor reveals load, run-state and certain electrical and mechanical faults without wiring into the drive.

The point isn't to drown the asset in sensors. It's to pick the few measurements that reveal the failure modes that actually take that machine down, baseline each one to its own normal, and trend it.

How to start without boiling the ocean

  1. Rank your assets by failure cost. What does an unplanned stop of each critical machine cost in lost production, repair, safety and spares lead time? Instrument from the top of that list down, only while the maths holds.
  2. Baseline each machine. Capture normal behaviour first, then set thresholds per asset — never from a generic table, or you'll drown in false alarms.
  3. Route every alert to an owner and a work order. A reading that doesn't become a planned intervention with an owner and a deadline changes nothing. Close the loop into your maintenance process.
  4. Design for the site. Edge buffering for power interruptions, hardware rated for dust and heat, and trending simple enough that your existing team acts on it without a full-time analyst.
  5. Prove it, then scale. A handful of critical machines that demonstrably avoid a breakdown will fund the rollout and earn the team's trust.

This is the same disciplined, decision-first approach we cover in why most vibration-monitoring projects fail — the failure is almost never the sensor, it's the scoping.

Why it fits South African mining specifically

Imported predictive-maintenance platforms often assume reliable power, fat connectivity and a resident vibration analyst — none of which can be taken for granted on a deep-level or remote SA mine. A system engineered for local reality buffers data through outages, runs hardware that survives dust and heat, and presents trends your existing team can act on, backed by local support. That's how predictive maintenance moves from a pilot to a habit.

At addanode this runs through our asset & condition monitoring and mining optimisation solutions on one addaNet platform — so reliability sits alongside your safety and production data instead of in a silo. Because we build both the hardware and the software in-house and support it locally, we scope for payback on your most critical assets first, not for sensor count — and it's built to survive the conditions your equipment actually works in.

Frequently asked questions

What is the difference between preventive and predictive maintenance?

Preventive maintenance services equipment on a fixed schedule regardless of its actual condition, so you risk servicing too early or too late. Predictive maintenance uses live sensor data — vibration, temperature, current — to detect a developing fault weeks ahead, so you intervene exactly when the machine needs it. Predictive cuts both unplanned failures and wasted servicing.

Does predictive maintenance replace oil analysis?

No — it complements it. Oil analysis is a valuable periodic snapshot of wear; real-time IoT monitoring watches the asset continuously between samples, catching faults that develop in the weeks between lab tests and alerting you the moment a trend turns. Keep your lab and add live sensing for full coverage.

Which mining assets should we monitor first?

Rank equipment by the cost of an unplanned stop — mills, crushers, conveyors, critical pumps, fans and gearboxes whose failure halts production or threatens safety. Instrument from the top of that list down, only while the payback holds. Starting with the most critical assets gives the fastest, easiest-to-justify return.

How quickly does it pay back on heavy mining equipment?

Usually fast, because the downtime it prevents is so expensive. When one unplanned stop of a critical mill or conveyor costs a shift of production, preventing even a handful of failures a year covers the monitoring system many times over. The business case is driven by the cost of the failures you avoid.

Do we need a full-time vibration analyst to run it?

Not for a well-scoped programme. Automated baselining and trend alerting let your existing maintenance team act on clear warnings, with specialist analysis reserved for the few most critical machines and the occasional deep diagnosis. Tools usable by your current team, plus local support, are what make it stick.