Manufacturing · Predictive maintenance
The bearing that fails at 2am was warning you for three weeks
Banao builds predictive maintenance models that read the vibration, temperature, motor-current, and time-series signals your assets already emit — and flag a developing failure days or weeks before the line stops.
The model scores each critical asset against your historian and PLC data, estimates how much running life is left, and pushes the alert into the maintenance workflow your team already uses. No new sensor estate unless the physics genuinely demands it.
RAK Ceramics— kiln and conveyor-drive signals scored for failure risk ahead of the maintenance window.
The first call is free · 45 minutes · no obligation
What we build
What a Banao predictive-maintenance deployment includes
Predicting a failure is only half the job. The alert has to reach the technician in time, with enough to act on. We own the model, the integration, and the workflow.
Failure prediction on signals you already have
Vibration, temperature, motor current, pressure, and time-series from your historian and SCADA. We model what your existing sensors already emit before we ever spec a new one.
Remaining-useful-life scoring per asset
Each critical asset gets a risk score and an estimated window, so maintenance is scheduled against evidence instead of a fixed calendar that swaps healthy parts and misses sick ones.
Alerts inside the maintenance workflow
Warnings land in your CMMS and work-order system and on the plant dashboard — not in a separate tool the maintenance team never opens.
Failure-mode triage, not just an alarm
The alert carries the likely failure mode and the signal evidence behind it, so the technician knows what to inspect before walking to the machine.
Retrofit sensing where an asset is blind
Where a critical asset emits no usable signal, we spec the minimum vibration or thermal sensing to close the gap and integrate it with 1990s PLCs and SCADA via retrofit.
Continuous learning from closed work orders
Confirmed catches and false alarms feed back from the closed work order, so the model sharpens against your real failures each maintenance cycle instead of drifting.
Receipts
Where this is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Kiln and drive signals scored for early failure
Heavy ceramic kilns and conveyor drives fail expensively and at the worst time. Banao modelled the vibration and thermal signals these assets already produce and scored each one for failure risk ahead of the planned maintenance window.
Historian data turned into early failure alerts
The plant had years of historian data and no model reading it. Banao trained on the recorded run-to-failure events, then wired the scores into the existing work-order system so alerts reached technicians where they already work.
Dogfooding
We run our own company on the same discipline
Instrument everything, alert before the thing breaks, and close the loop with what actually happened — that is exactly how Banao runs its own ~300-person engineering operation, not just how we build for you.
InterviewGod screens our own engineering hires, and Vikaas runs our own demand-generation pipeline. A model that has to hold up inside our own operation every working day is hardened long before it scores an asset on your floor.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When predictive maintenance isn't worth building
A model is not the right answer for every asset. We will tell you before you spend on one:
- Run-to-failure assets: a cheap, redundant motor that swaps in twenty minutes is often cheaper to replace on failure than to model. We'll say so.
- No failure history: a model needs examples of things going wrong. On a young asset with no recorded failures, week one is data collection, not prediction.
- Sensor-blind and low-cost: if an asset can't be instrumented affordably and its failure doesn't stop the line, the honest answer is to leave it alone.
How we start
How we start — prove the signal before you build
We don't quote a maintenance program off an asset list. We look at your real data first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We pull a sample of historian data on your most failure-prone assets, test whether the failure actually shows up in the signal early enough to act on, and hand back a feasibility read and ROI maths — yours to keep. Credited against the build if you proceed.
- 02
Build
Model your critical assets, integrate with the historian, SCADA, and your CMMS, and wire alerts into the maintenance workflow. Any retrofit sensing is part of the deliverable.
- 03
Production & continuous learning
Live scoring with technician feedback from closed work orders, plus a plant dashboard. The model recalibrates against your real failures every cycle.
FAQ
Frequently asked questions
How much failure history do you need to start?
Enough recorded run-to-failure or repair events to learn the signal — often a year or two of historian data on the target assets. Where history is thin, the Discovery Sprint establishes whether staged data collection or physics-based features get you to a usable model.
Do we have to install new sensors?
Usually not at first. Banao models the vibration, temperature, current, and pressure signals your assets already emit and only specs retrofit sensing where a critical asset is genuinely blind. The week-one audit settles which is which.
Which assets are the best candidates?
Assets whose failure stops or slows the line, that fail often enough to have a history, and that emit a measurable signal — motors, pumps, compressors, drives, kilns, gearboxes. We rank your asset list by failure cost and data quality in the Sprint.
Does it integrate with our CMMS and SCADA?
Yes. Alerts are pushed into your existing CMMS and work-order system, and the model reads from your historian and SCADA, including retrofit onto older PLCs. Integration is part of the build deliverable, not a separate project.
What happens when the model raises a false alarm?
The technician closes the work order with what they actually found, and that outcome feeds back into the model. False alarms and confirmed catches both sharpen it, so precision improves cycle over cycle rather than eroding trust.
Get started
Bring us your worst repeat failure
Pick the asset that keeps stopping the line. In 45 minutes we'll tell you whether its failure shows up in the data early enough to act on — and what predicting it would take.
Book a Discovery Sprint