bellosani2drescue
About Candidate
Designed and developed a Digital Twin framework integrated with a Health and Usage Monitoring System (HUMS) to enable predictive maintenance and reliability analysis for aircraft systems.
Built data-driven models combining physics-based simulations and machine learning (CNN-LSTM architectures) to monitor system degradation, estimate Remaining Useful Life (RUL), and detect anomalies under varying operational conditions.
Engineered data pipelines for real-time and simulated sensor data, transforming raw inputs into actionable maintenance insights aligned with Condition-Based Maintenance (CBM) strategies.
Performed reliability assessments, failure mode analysis, and system performance evaluation to support maintenance optimization and reduce downtime.
Collaborated across engineering domains to align model outputs with maintenance practices and improve decision-making beyond traditional Aircraft Maintenance Manual (AMM) approaches.
