A global wind farm operator managed critical assets without access to engineering blueprints, faced premature gearbox failures, and spent months validating performance through physical testing.
The challenge wasn't a lack of data. It was the inability to predict failures where conventional monitoring and physical sensors reached their limits.
By embedding first-principles engineering into AI, LTTS transformed fragmented telemetry into an intelligent virtual sensor ecosystem, delivering:
- 90% accuracy in predicting hidden crack initiation between gear teeth
- 75% faster certification, compressing validation cycles from months to weeks
- Continuous operational visibility through virtual sensors that compensate for hardware failures
- Physics-driven predictions without relying solely on historical failure data
This isn't another dashboard layered onto sensor data. It's engineering intelligence embedded directly into your operations.
Download the case study to learn how the same physics-driven approach is delivering predictable, scalable outcomes across wind energy, automotive, and critical infrastructure.