Synthetic Data for Predictive Maintenance
Describe the fleet, for example 40 wind turbines with gearbox and blade-pitch failures over six months of 10-minute data, and Misata Studio simulates machines that actually wear out. Each machine lives through run-to-failure cycles: a life drawn from a Weibull distribution, a failure mode that decides which sensors drift and how, then downtime, repair and a fresh life. The sensors, ranges and failure modes are written for your kind of machine and checked by code, and remaining useful life is the true time to the next failure, so a model trained on it can learn.
Updated 2026-09-24.
Predictive maintenance dataset for 40 wind turbines with gearbox and blade-pitch failures, 6 months of 10-minute SCADA data.
Studio builds this on one of its tested archetypes: the mechanics below are fixed and verified, and what the business sells, the names and the wording are written for yours.
Start from thisWhat Studio builds
sites and assetsWhere the machines are and what they aresensor_readingsLoad-driven baselines, a daily ambient cycle, noise that grows as the machine degradesfailure_eventsWhen each machine failed and by which modework_ordersDowntime and repair after each failureWhat holds, and how you know
These are checked on the finished data, not assumed. Each dataset comes with a certificate listing what you asked for and whether it was met. How we verify.
- Remaining useful life is the real time to the next failure, not an estimate
- Each failure mode drives its own sensors, so the label can be diagnosed from the data
- Readings have the flaws real telemetry has: dropouts, spikes and stuck sensors
- Sensors and ranges are written for your machine type and validated before use
- Every foreign key points at a row that exists, checked on the finished data
- Every count, total, rate, share and date window you state is applied exactly, or listed as not applied
- A certificate lists each requirement and whether the data meets it
A 100-machine run-to-failure dataset with exact remaining-life labels and a published baseline. Public domain.
See the sampleHave a schema, specialised rules or a large volume and would rather hand it off? Ask us to build it for you.
Frequently asked
Do I need real predictive maintenance data to generate this?
Is the generated predictive maintenance data privacy safe?
Can I control the numbers, like rates and totals?
Can I use my own schema?
Which formats can I export?
Is the physics validated against real machines?
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