Time-based maintenance is a compromise: you service equipment on a fixed calendar schedule because you do not know when it will actually need attention. That means servicing some equipment unnecessarily early, wasting parts, labour, and production time, and other equipment too late, after a fault has already developed into a failure. The gap between those two errors is where predictive maintenance operates.
A predictive maintenance model watches the sensor signals that indicate developing faults, rising bearing temperature, changing vibration spectrum, increased current draw, and detects the deviation from normal behaviour weeks or months before the equipment fails. The result is maintenance scheduled when the equipment actually needs it, based on its actual condition rather than an arbitrary calendar interval. RaftLabs builds these models end to end: sensor data pipeline, anomaly detection model, remaining useful life estimation, and the CMMS integration that turns a model prediction into a work order your maintenance team can act on.
For asset-intensive operations, the payoff is not marginal. Published research puts the range clearly.
- 30-50%
- Less machine downtime
- McKinsey Global Institute, IoT report
- 10-40%
- Lower maintenance costs
- McKinsey Global Institute, IoT report
- 10-20%
- Higher equipment uptime
- Deloitte, Predictive Maintenance and the Smart Factory
Those figures translate into fewer unplanned shutdowns, lower parts costs, and maintenance labour aimed at equipment that actually needs attention. The catch is that they only hold when the model is trained on the right signal and the alerts reach the people who act on them. That is the part most implementations get wrong, and the part we build for first.
Most plants already run preventive maintenance: a calendar interval set once from manufacturer guidance. It beats running to failure, but it services healthy equipment too early and still misses faults that develop between intervals. Predictive maintenance replaces the interval with the asset's actual condition.
Preventive versus predictive maintenance
| Preventive (calendar-based) | Predictive (condition-based) |
|---|
| Trigger | A fixed time or usage interval, set once and rarely revisited | A change in sensor behaviour that signals a developing fault |
| Timing error | Services healthy equipment early and fails equipment late | Schedules work when the asset's condition actually calls for it |
| Data needed | A maintenance calendar and manufacturer intervals | Sensor history plus labelled failure events for the assets that matter |
| Main risk | Wasted parts and labour, plus surprise breakdowns between visits | False alarms when thresholds are untuned, so alerting is tiered and reviewed |
| Best fit | Low-value assets where a sensor is not worth the cost | Critical rotating and electrical assets where downtime is expensive |
We tell you which assets belong in which column. Not every machine earns a model, and putting a sensor on a cheap, easily replaced part rarely pays back.
Capabilities
What we build
01Sensor data anomaly detection
Unsupervised anomaly detection trained on your equipment's normal operating data, not generic benchmarks that ignore your machine configuration and loads, with the model matched to the fault signature. Each model outputs a continuous anomaly score tiered into watchlist flag, maintenance alert, and urgent work order, cutting false positives while catching developing faults early.
- Built with
- Statistical process control · Isolation Forest · Autoencoders
02Remaining useful life prediction
Remaining Useful Life (RUL) regression models that estimate how many operating hours, cycles, or days remain before a specific failure mode occurs, giving maintenance planners a time horizon rather than just an alarm. Output is a P10/P50/P90 range, not a point estimate, so the planner sees both urgency and planning window, and predictions refresh as new sensor data arrives so spare parts are ordered before the window, not after.
03Failure mode classification models
Supervised classification models that identify which specific failure mode is developing, not just that something is off, so technicians arrive with the right parts and tools. For rotating equipment, the classifier separates bearing defects, rotor imbalance, shaft misalignment, and winding degradation from their vibration and current signatures, outputting a probability vector so the engineer knows what to inspect first. Severe class imbalance is handled with oversampling and weighted loss functions.
- Built with
- SMOTE
04CMMS work order integration
Automated work order creation in your CMMS when an anomaly score or RUL estimate crosses threshold, converting model predictions into maintenance actions without a planner watching a separate dashboard. Each work order carries the failure mode, recommended inspection action, and sensor evidence, suppression logic prevents duplicate orders while an inspection is pending, and inspection outcomes feed back into model training.
- Built with
- IBM Maximo · SAP PM · Infor EAM · UpKeep
05Predictive maintenance dashboard
An operational dashboard built for maintenance engineers and plant managers who need to act on model outputs without understanding the underlying ML. The fleet health view shows every asset with a status indicator, anomaly score, RUL estimate, and open work orders in a 30-second scan, drill-downs overlay anomaly scores on 72-hour sensor series, and a prioritisation view sorts assets by urgency and business impact, with a simplified mobile view for supervisors on the plant floor.
06Model performance monitoring and retraining
Automated monitoring that compares model predictions against actual inspection outcomes, tracking precision and recall over time, because a model accurate at commissioning degrades as equipment ages and conditions change. Drift tests run weekly on incoming sensor distributions, and when metrics drop or drift is detected, retraining triggers automatically and is validated against the current production model before promotion, with a monthly performance report for your maintenance team.
- Built with
- MLflow
Most predictive maintenance projects do not fail on the model. They fail on thin data, false alarms, drift, and a last mile that never reaches the technician. We design for each of these from the first engagement, not after the pilot stalls.
- Thin failure data
- Most plants have years of normal-running data and only a handful of recorded failures. We start with anomaly detection off the healthy baseline, which needs no failure examples, and add supervised failure-mode models only where the labelled events support them.
- False-alarm cost
- An alert that sends a technician to a healthy machine erodes trust fast, and a team that stops trusting the model stops acting on it. We tier every score into watchlist, alert, and work order, and tune thresholds against your real inspection outcomes.
- Model drift
- A model accurate at commissioning decays as equipment ages, loads shift, and sensors are swapped. We compare predictions against actual outcomes weekly and retrain against the live population before promoting a new model.
- The last mile
- A prediction that sits on a dashboard nobody opens changes nothing. We push work orders into the CMMS the maintenance team already uses, with the asset ID, fault type, and recommended action attached.
Unexpected equipment failures are not inevitable.
Tell us what equipment you are trying to protect, what sensors you currently have, and what your current maintenance strategy is. We will assess whether a predictive maintenance model is worth building and what failure modes it can realistically detect.
