Predictive Maintenance With IoT: A Practical Guide for Australian Manufacturers
Last updated: June 2026
Quick answer: Predictive maintenance uses sensors (vibration, current, temperature, acoustic) and analytics to detect equipment degradation weeks before failure, replacing both run-to-fail chaos and wasteful fixed-schedule servicing. Australian pilots typically cost AUD $20,000–$60,000 on a critical asset set, with ROI driven by avoided unplanned downtime — which costs most plants thousands of dollars per hour.
The three maintenance strategies, honestly compared
| Strategy | How it works | Hidden cost |
|---|---|---|
| Reactive (run to fail) | Fix it when it breaks | Unplanned downtime at the worst moment, collateral damage, overtime, air-freighted parts |
| Preventive (calendar) | Service every N weeks regardless | Servicing healthy machines; failures still occur between services |
| Predictive (condition) | Sensors watch each asset; intervene on evidence | Upfront system cost — then maintenance happens exactly when needed |
The economics hinge on one number: your cost per hour of unplanned downtime on a critical line. Calculate it (lost output + labour idle + restart scrap). If it's four figures per hour — and for most production lines it is — a single avoided failure pays for a pilot.
What gets measured, and what it catches
Vibration analysis (bearings, imbalance, misalignment — the workhorse for rotating equipment), motor current signature (electrical and load faults without touching the machine), temperature trends, acoustic/ultrasound (leaks, early bearing noise, partial discharge in electrical gear), and oil quality on gearboxes. The same condition-monitoring pattern extends to electrical infrastructure — continuous partial-discharge monitoring catches switchgear faults before outages.
The realistic path (not the vendor-deck version)
- Pick 3–10 assets that hurt — critical to throughput, history of failures, expensive to fix in a panic. Don't instrument the whole plant on day one.
- Baseline first. Weeks of data establish each machine's normal before any "AI" can call abnormal. Skipping this stage is why pilots drown in false alarms.
- Start with thresholds and trends, add ML where it earns it. Most early wins come from honest trend lines, not deep learning. Models become valuable once data history exists.
- Integrate with how work actually happens — alerts must land in your CMMS/work-order flow with evidence attached, or technicians will (rightly) ignore them.
- Count the saves. Every caught fault gets documented with avoided-cost estimates. That document funds the rollout.
What it costs (Australia, 2026)
Pilot on 3–10 assets including sensors, gateway, platform, and alerting: AUD $20,000–$60,000. Per-asset expansion afterwards: a few hundred to a couple of thousand dollars per machine depending on sensing depth. Legacy machines without data interfaces join via retrofit sensing — age is not a blocker.
Frequently asked questions
Does predictive maintenance need "AI"?
Not to start. Trending and thresholds catch the majority of developing faults. ML earns its place later, on assets with rich history and subtle failure modes — be suspicious of anyone leading with the model instead of the sensing.
How long until ROI?
Typical pattern: baseline in months 1–2, first caught fault within 3–9 months. One avoided line-down event usually clears the pilot cost.
Can old machines be monitored?
Yes — vibration, current, and temperature sensing bolt onto anything that spins or draws power. Some of the best ROI is on 30-year-old equipment nobody dares lose.
Further reading
- Industrial IoT and condition-monitoring systems
- AI and ML for predictive analytics
- Smart factory monitoring case study
- 13kV RMU fault-detection case study
Incendio Solutions builds condition-monitoring systems for factories and electrical infrastructure — from sensing hardware to the dashboards your team actually uses. Scope a pilot.