CMMS for Cement Plants

Cement plants run on rotating equipment that fails predictably — bearings, liners, drives, gears — but only if the failure data behind it is actually structured. AssetAI is built to capture that data from the floor and turn it into decisions on repair, replace and spend.

How the failure data gets trustworthy

Most plants already log breakdowns; few can trust the Pareto built on top of them. The reason is simple: a work order gets closed without anyone recording why the bearing seized or what fixed it. AssetAI blocks that shortcut — a breakdown or corrective work order on a kiln, mill or crusher line cannot close without a failure mode, cause and remedy attached.

• Over months, this turns scattered kiln/mill/crusher stoppages into a real failure Pareto, not an anecdotal list.
• Downtime cost per hour is stored per asset, so the same Pareto automatically separates raw mill losses from kiln losses from cement mill losses — no extra tagging or configuration needed.
• Emergency-severity events like a kiln drive seizure skip the approval queue entirely and raise the work order immediately, because a stopped line doesn't wait for sign-off.

This is also the data an RRR verdict draws on when deciding whether a girth gear or mill drive is worth another repair cycle versus replacement — cost, failure trend, downtime cost and age, computed together instead of argued in a meeting.

Where this fits into a broader reliability programme

A CMMS on its own doesn't fix reliability — it gives the plant the record-keeping that reliability practices like TPM or condition-based maintenance depend on. For cement specifically, that means:

• Meter-driven PM on run-hour equipment — crusher liners, mill drives, kiln support rollers — where the schedule holds the meter unit, interval, and last/next reading, and comes due the moment a logged reading crosses target.
• Condition-based PM on rotating drives using a monitored parameter and threshold — a mill or crusher drive vibration limit of >4.5 mm/s is a literal field on the schedule, not a marketing claim, and once a reading breaches it a PdM work order is generated.
• A five-level asset breakdown (Equipment → Assembly → Sub-Assembly → Component → Part) so a kiln can be tracked down to shell, tyre or girth gear, and failure history rolls up cleanly at whichever level matters.

None of this depends on a live DCS or SCADA feed — readings can be logged manually, by photo, over WhatsApp, or via API, which matters in plants where instrumentation coverage is inconsistent across sections. If you're new to the category, the what is a CMMS page covers the basics, and OEE explained is worth reading before you judge kiln or mill OEE numbers — they're only as good as the production log feeding them.

Getting AMC and contractor work under control

Refractory relining, gearbox overhauls and OEM service visits are usually tracked in email threads and register books. AssetAI runs them as a structured loop — Requested → Visited → Quoted → ... → Closed — so a contract coordinator can see what's outstanding without chasing a contractor for status. Combined with criticality scoring (High/Medium/Low) on kiln and mill assets, this gives planners a way to prioritise which contractor jobs actually protect production.

See how this looks on your own kiln, mill and crusher data — book a 30-minute demo instead of a slide deck, or browse the full feature set and other industries AssetAI is running in.

Cement plants run 24x7 on equipment that generates dust, heat and vibration in equal measure — conditions where a login screen or a paper meter-reading log quietly breaks down within weeks. The sections below cover how AssetAI handles PM scheduling and plant structure without assuming ideal floor conditions, and where it stops short of a cement-specific engine.

Scheduling PM around run-hours and condition, not the calendar

Kiln, mill and crusher components don't wear out on a fixed monthly cycle — a crusher liner or mill drive bearing wears with hours run and load, not days elapsed. AssetAI's PM schedules reflect that directly:

• Usage-based schedules hold a meter unit, interval, and last/next reading; the work order goes due the moment a logged reading crosses the target — useful for crusher liners, mill drives and kiln support rollers.
• Condition-based schedules carry a monitored parameter and an alarm threshold — a mill or crusher drive schedule set at vibration > 4.5 mm/s generates a PdM work order the moment a logged reading breaches it.
• Readings can be entered manually, via photo, or over WhatsApp/API, which matters when the person taking a vibration or hour-meter reading is standing next to a running kiln, not at a desk.

This is meter-and-threshold logic, not a live DCS/SCADA feed — condition monitoring here is scheduled and logged, not streamed. If your plant already runs continuous vibration or temperature monitoring, [VERIFY: any DCS/SCADA/historian integration] before assuming it plugs in directly.

Modelling a kiln down to the component that actually fails

A "kiln" as a single asset record is useless for failure analysis — the girth gear, tyre, shell and support rollers fail for different reasons at different intervals. AssetAI models the plant as a Location tree (plant → area → line → location) alongside a five-level Asset EBS (Equipment → Assembly → Sub-Assembly → Component → Part), so a breakdown can be logged against the actual failing part rather than the kiln as a whole. That structure is also what makes criticality scoring and the RRR verdict meaningful at the component level rather than a blanket judgment on the whole machine — see /features for how these connect.

Two things worth setting expectations on up front:

• There's no cement-specific data model — no kiln shell ovality field, no refractory thickness log, no clinker/cooler-specific attributes. It's a generic asset/meter/failure structure applied to cement equipment, configured through the EBS above rather than pre-built cement fields.
• Kiln and mill OEE is only as reliable as the production log feeding it — an asset with no production data logged simply gets skipped in OEE reporting rather than showing a misleading number. Background on the metric itself is on Wikipedia's OEE page.

Capturing nameplate data without re-typing it

Large rotating equipment — kiln drives, mill motors, crusher gearboxes — often carries a nameplate that's scratched, coated in dust, or in a position nobody wants to climb to twice. AssetAI's nameplate OCR reads serial number, year, capacity and description from a rating-plate photo directly into the asset record, avoiding a manual transcription step that's usually where asset registers start drifting from reality. It's a small mechanism, but on a plant with hundreds of motors and gearboxes across kiln, mill and crusher lines, it's the difference between an asset register that matches the floor and one that doesn't.

If you're building out a broader reliability programme alongside this — audits, statutory checks, ISO alignment — see /standards for how AssetAI's structure lines up with frameworks like ISO's standards catalogue, or book a demo to walk through a kiln or crusher line specifically.