HYPER-PREDICT: A Physics-Informed, Uncertainty-Calibrated Decision Engine for Real-Time Predictive Maintenance in High-Performance Motorsport Systems

We present HYPER-PREDICT, a hybrid framework for real-time estimation of Remaining Useful Life (RUL) in high-performance mechanical systems operating under motorsport conditions. The architecture integrates six interconnected components: a multiseed LSTM ensemble for noise-robust prediction; symbolic regression for interpretable physical-law extraction; a Physics-Informed Neural Network (PINN) incorporating the Archard wear law as a soft constraint; a TimeGAN-based generator for rare-failure synthesis; split conformal prediction for distribution-free uncertainty quantification with formal coverage guarantees; and ONNX-based edge deployment with sub-millisecond latency.

On a synthetic brake-disc telemetry dataset (50,000 samples, 15 engineered features), the system achieves MAE = 2.69 laps on a 300-lap scale (less than 1% relative error), R² = 0.9953 for the closed-form symbolic formula RUL ∝ ⁴√(T_disc / Downforce), empirical 99% conformal coverage of 0.9926, and p99 inference latency of 1.57 ms — well below the 50 ms operational budget for ECU-level deployment. The exported ONNX model is 845 KB.

We deliberately report a negative result: synthetic data augmentation via TimeGAN does not improve downstream classification recall when sufficient critical-regime data is available, with measured Δrecall of −0.050. This methodologically transparent disclosure is intended to encourage rigorous reporting in the predictive-maintenance literature.

The complete pipeline is reproducible from a public Kaggle notebook with fixed random seeds. Source code is released under MIT license. All results derive from a synthetic dataset; validation on real motorsport telemetry is the natural next step and the subject of ongoing industrial collaboration efforts.