JolokLabsEngineering & Computing
Predictive Maintenance and Turbofan RUL with Python
Build and audit a turbofan engine prognostics project using the NASA C-MAPSS dataset. Master degradation modeling, leak-free sequence windowing, RUL regression, baseline comparisons, and explainable AI with SHAP.
Syllabus & Learning Objectives
Each module follows the JolokLabs methodology: physical question first, numerical method second, and reproducible error check third.
Module 1
Defining the Maintenance Question (Classification vs anomaly detection vs RUL regression)
Module 2
NASA C-MAPSS Preprocessing (Sensor filtering, operating conditions, normalization)
Module 3
Preventing Data Leakage (Proper train/validation splitting across distinct engine units)
Module 4
Establishing Strong Baselines (Linear models, exponential degradation, naive benchmarks)
Module 5
Machine Learning for RUL (Random Forest, Gradient Boosting, and sequence modeling)
Module 6
Prognostics Evaluation Metrics (RMSE, MAE, asymmetric penalty score, and error analysis)
Module 7
Model Explainability with SHAP (Feature importance vs causal attribution, sensor impact)
Module 8
Capstone: Defensible Prognostics Dashboard (Reproducible inference with honest limitations)
Why JolokLabs Courses Are Different
Physical Intuition First
Start every engineering problem with governing mechanics, units, and boundary conditions before opening code.
Reproducible Code & Data
Real datasets (e.g. NASA C-MAPSS, ArduPilot SITL) without data leakage, synthetic shortcuts, or arbitrary assumptions.
Human-Audited Verification
Master error diagnosis, residual validation, and defensible engineering reports you can explain to a committee or manager.