Anomaly Detection vs Classification: Which ML Approach When?

You have vibration data on your crushers, temperature readings on your mill motors, and current draw on your conveyors. Your team is ready to try machine learning. But here is where most sites stall: which ML approach do you actually need?

The answer is more useful than "it depends."

Two Approaches, Different Starting Points

Anomaly detection learns what "normal" looks like and flags anything that deviates. It is unsupervised. Point it at your SAG mill motor current during healthy operation and it will flag when something shifts, even failure modes nobody has seen before.

Classification is trained on labelled examples of specific faults. Feed it thousands of vibration signatures tagged as "inner race defect" or "misalignment" and it will diagnose future faults by type. Precise, actionable, but only for failures it has been trained on.

The Decision Is Simpler Than You Think

Ask yourself one question: do you have labelled failure data?

If yes, and you have 50+ examples per failure mode, classification will give you specific diagnoses. If no, and most sites do not when starting out, anomaly detection gets you running in weeks, not months.

The smart play? Start with anomaly detection. Graduate to classification as your labels accumulate. Every anomaly your team investigates and tags becomes a training label for future classifiers. It is not either/or. It is a maturity path.

Data Does Not Push. It Pulls.

The most common reason we hear for delaying ML projects in mining is "we need more data." It is backwards.

When you deploy a model, even a simple anomaly detector, you create the pull for better data. Operators start tagging alerts. Engineers add sensors where the model is blind. Data quality improves because someone is actually using it.

Start with what you have. The data will follow.

Get the Full Decision Framework

We have built a free interactive reference card with the complete side-by-side comparison, a decision flowchart, and real examples from haul trucks, SAG mills, conveyors, and crushers.

What Is Your Starting Point?

Whether you are sitting on years of labelled vibration data or you have just installed your first sensors, there is a clear path forward.

Ready to assess which ML approach fits your operation? Request a maturity assessment and we will map your data, your assets, and your quickest wins.