Predictive Maintenance Software
A model is useful only when a measurable condition changes before failure and maintenance can respond in time. RaftLabs develops predictive maintenance software around one equipment class and failure mode, joining condition data with work history, validating warning performance, and sending evidence into the CMMS for technician review.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Are critical assets still failing between scheduled services while healthy parts are replaced early?
Do condition alerts reach a dashboard without enough evidence or lead time for a technician to act?
Plain answer
Predictive maintenance software uses condition and work history to spot equipment faults before failure. RaftLabs develops one warning model, validates warning time and false alarms, and sends evidence to the CMMS for review. A focused first release starts at $30,000 and takes 12 to 18 weeks.
The monitoring system was technically right. The temperature crossed its threshold, the alarm fired, and the fault appeared on a dashboard. Maintenance still had no time to inspect the asset or move production.
Predictive maintenance has to change the work plan before the failure. That means finding a reliable precursor, proving the useful warning window, and putting enough evidence into the technician's existing queue.
First scope
RaftLabs does not currently publish a named predictive-maintenance outcome case study. The first engagement therefore tests whether the available condition data supports a useful warning against the buyer's own maintenance history before broader development is recommended.
Calendar maintenance or simple thresholds are better when they already control the risk at lower cost.
The asset is critical enough that avoidable downtime or early replacement carries material cost.
Condition and maintenance records can be aligned by stable asset identity and time.
A technician can inspect within the warning window and record what was found.
The asset is cheap, redundant, or easier to replace after failure.
The relevant condition is not measured and cannot be instrumented safely.
Maintenance outcomes are not recorded against stable assets or components.
Scope
| Preventive maintenance | Predictive maintenance | |
|---|---|---|
| Trigger | Calendar time, usage, or manufacturer interval | A measured change associated with a developing fault |
| Best fit | Known wear intervals and inexpensive scheduled work | Critical assets where condition gives useful warning |
| Data | Asset register and service schedule | Condition, operating state, maintenance, and failure history |
| Main risk | Healthy parts replaced early or faults missed between visits | False alarms or warning too late for maintenance to respond |
| Decision | Service when the interval arrives | Inspect when evidence crosses the approved warning rule |
Many fleets need both. Prediction belongs only on asset and failure combinations where the extra signal changes cost, risk, or uptime.
How it works
Prove one warning and response path before covering more equipment.
Define the equipment class, failure mode, consequence, warning window, inspection step, and maintenance owner.
Join sensor, operating-state, maintenance, component, inspection, and failure records by asset and time. Inspect missing periods and changed sensors.
Backtest alerts by lead time, missed faults, false alarms, operating regime, and the team's capacity to inspect them.
Deliver evidence into work orders, record technician findings, monitor drift, and expand only when the first warning remains useful in operation.
A credible pilot should show which historical events would have triggered an alert and how much warning they provided. It should also show missed faults, false alarms entering the maintenance queue, and whether a technician could have changed the outcome. The result should expose assets or operating regimes where the data is insufficient.
That evidence is more useful than a generic claim about reducing downtime. RaftLabs does not publish a predictive-maintenance percentage because performance depends on the equipment, failure mode, sensors, maintenance records, and response window.
Scope and price
Begin with one equipment class, one actionable failure mode, historical validation, and one CMMS path.
We test whether the signal supports useful warning before committing to more asset classes or automatic work orders.
Starting investment
Starts at $30,000
A focused first release usually takes 12 to 18 weeks. Instrumentation, safety controls, and sparse history move the estimate most.
Signal audit first
We inspect representative condition and maintenance history before fixing the production scope. If the precursor or response window is not credible, we say so.
Fixed-price phase
Once the asset, failure mode, acceptance measures, CMMS boundary, and handover are agreed, the phase price is locked in writing.
Related services
Manufacturing predictive analytics
Use forecasting and machine learning across production, quality, maintenance, and operational planning. This broader service fits teams comparing several factory decisions.
IoT development
Connect devices, sensor streams, edge software, and cloud services before prediction becomes the next layer.
Demand forecasting
Predict product or material demand for inventory, purchasing, and capacity planning rather than equipment condition.
The useful data depends on the failure mode. A first audit links condition signals such as vibration, temperature, pressure, current, alarms, or operating state with stable asset IDs, component changes, maintenance work, inspections, and confirmed failures. Data volume alone does not prove the required precursor exists.
Sometimes. Anomaly detection can learn a healthy operating baseline without many failure labels, but an anomaly is not automatically a fault. A technician still needs to assess whether the change is useful. Failure classification or remaining-life estimates need stronger outcome history and enough comparable events.
The integration should send the asset, suspected failure mode, supporting condition evidence, urgency, and recommended inspection into the maintenance workflow. Duplicate suppression prevents repeated orders while one inspection remains open. Technician findings then become feedback for the warning policy and model.
Measure whether the warning arrives early enough to change the maintenance plan, how many real faults it identifies, how many false alarms consume technician time, and whether the team records inspection outcomes. Compare the result with the current preventive or reactive baseline.
A focused first release for one equipment class and failure mode starts around $30,000 and usually takes 12 to 18 weeks. New sensors, several operating regimes, sparse maintenance history, safety controls, multiple asset classes, and deeper CMMS workflows increase the scope.
Work with us
Bring its condition history, maintenance records, failure mode, and response window. We will test whether prediction is justified.