Advanced Predictive Maintenance Forecasting Model

Construct a comprehensive predictive maintenance framework using advanced machine learning techniques that can handle multivariate sensor data with complex failure modes. Develop a hybrid approach combining survival analysis, gradient boosting machines, and deep learning neural networks to predict equipment failure probabilities. Create a modular system that supports real-time inference, uncertainty quantification, and automated feature engineering from time series sensor data. Include robust handling of missing data, concept drift detection, and interpretable risk scoring.