Predictive MaintenanceSolutions
Spot the warning signs in vibration, temperature and run-time data so a machine is serviced before it fails mid-shift.
Predictive Maintenance Solutions: what the work involves
Factories, fleets and facilities usually maintain equipment in one of two ways: after it breaks, or on a fixed calendar. Breakdowns halt lines and ruin delivery promises, while calendar servicing replaces parts that still have life left and still misses the failure that develops between visits. Maintenance records are often on paper or in free-text notes, so patterns across machines stay invisible.
We start with the data you can realistically collect: PLC readings, retrofit vibration or temperature sensors, run hours, fault codes and the work orders describing past failures. After aligning sensor streams with failure dates, we engineer features like rolling averages, rate of change and spectral content, then train a model to estimate either the likelihood of failure within a window or the remaining useful life. Where failures are too rare to learn from, anomaly detection on healthy behaviour is used instead. Alerts reach the maintenance planner with the suspect component and the supporting readings, and technicians' findings are logged so the model improves.
Core features
Sensor and log ingestion
Readings from PLCs, IoT sensors and machine logs are collected, cleaned and aligned in one store.
Failure history mapping
Past work orders and breakdown notes are structured, with language models helping to read free-text records.
Failure risk or remaining life
Models estimate the chance of failure in a coming window, or a range for remaining useful life of a component.
Healthy-baseline monitoring
When failures are rare, the system learns normal behaviour and flags departures from it.
Planner alerts with evidence
Alerts name the machine and likely component, and show the readings that triggered them.
Technician feedback capture
A mobile form records what was found on inspection, giving the model ground truth to learn from.
What we get right before launch
Few failure examples
Well-maintained equipment rarely fails, leaving little to learn from. We state this early, begin with anomaly detection and condition thresholds, and build supervised models only when enough failures are recorded.
Sensor installation and reliability
Bad mounting, drift and gaps in data spoil results. We plan sensor placement with your engineers and add data health checks so a failed sensor is not read as a healthy machine.
Trust on the shop floor
Technicians ignore alerts that cry wolf. We introduce the system in advisory mode on a few assets, review each alert with maintenance staff, and widen only when it earns confidence.
Tools and technology
- Python
- scikit-learn
- XGBoost
- PyTorch
- MQTT
- InfluxDB
- FastAPI
- Grafana
Common questions, answered
Do we need to install new sensors?
Sometimes. Existing PLC data or fault logs may be enough to begin. If not, low-cost retrofit sensors can be added. We assess your equipment and records before recommending hardware.
How early can it warn us?
That depends on how the failure develops. Some faults show weeks of drift, others appear suddenly with little warning. We test on past failures to estimate realistic lead times and tell you which are not predictable.
Can it work with older machines?
Often yes, using clamp-on vibration, temperature or current sensors and a small gateway. Very old equipment may be limited by what can be measured, and we explain the constraints upfront.
Does it replace scheduled maintenance?
Not entirely. Many safety-critical or regulatory inspections must continue. The model helps prioritise and time work, so scheduled tasks are supplemented, and some can be adjusted on evidence.
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Inventory Forecasting Solutions
Know what to reorder, how much and when, with safety stock set from your real lead times and demand variability.
Ready to start your Predictive Maintenance Solutions project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.