Predictive maintenance for heavy equipment
A rising temperature or a change in vibration can give your team time to investigate. Talpeye helps assess those warnings and plan the repair, including the parts, technician and time the job needs.
Example repair
Equipment condition review
A sensor trend moves away from the asset’s recent operating baseline.
- Evidence
- Readings and operating context
- Alert
- Uncertainty in view
- Action
- Investigation before approval
Investigate the supporting signals and uncertainty before proposing an intervention and maintenance window.
How it works
Check the readings you have
Choose the equipment and faults you want to study. Check how often readings arrive, what is missing and how the machine was running. Some faults cannot be detected from the available sensors.
Judge the warning
Compare the alert with recent readings and normal operating conditions. Track missed faults, false alerts and how long warnings last to see whether they help your team decide.
Plan the repair
Use the findings to decide what to inspect or repair. Check parts, technician availability and production time before approving the job. Keep the uncertainty in the data visible during that decision.
What we need to get started.
Sensor history
Time-stamped readings, channel definitions, units, sampling intervals and known gaps.
Asset context
Operating modes, utilisation, component details and maintenance events.
Evaluation records
Confirmed failure events, healthy operating periods and the actions taken in response to alerts.
What to measure in a pilot.
We checked our alerts against historical data. A pilot needs to check missed failures, warning times and false alerts in your operation, then test whether the team can act on them. The links below describe the datasets used in the research.
- Failure events detected and missed
- Warning time and alert persistence
- False alerts and investigation workload
References and further reading
Petrobras 3W · public oil-well event datasetScania Component X · Scientific DataCommon questions.
Does predictive maintenance guarantee a failure date?
No. An alert flags a possible fault based on the readings available. Your team needs to check it against how the machine is being used. Warning times measured in a study will not be the same for every machine.
What should we measure in a pilot?
Agree what counts as a failure and how long the test will run. Count missed faults and false alerts as well as correct warnings. Record how much time the team spends investigating and what happens after each warning.
Can testing alerts on historical data prove downtime savings?
Historical data can show whether alerts matched recorded failures. To measure downtime savings, you need to compare actual repairs and production stops with the way the site worked before the pilot.
Show us how you work today.
Tell us what equipment you run and where maintenance gets held up. We’ll show you how Talpeye could help and what a pilot would involve.
Book a walkthrough