Engineered a predictive maintenance model for turbofan engines, estimating Remaining Useful Life (RUL) using ensemble-trained LSTMs. Combined deep learning with time-series sensor data to forecast component degradation, optimizing maintenance cycles and reducing downtime.
YEAR
2021
DOMAIN
Machine Learning
Predictive Maintenance
Prognostics
TECH STACK
Python
TensorFlow
About the project
Designed a hybrid prognostics system integrating neural networks and probabilistic models to predict failure timelines of industrial engines. Trained on thousands of real-world operational cycles, the system leveraged raw sensor data and statistical inference to deliver high-accuracy, cycle-aware RUL estimations for critical aerospace components.
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Predictive Modelling of Turbofan Engines
OVERVIEW
Engineered a predictive maintenance model for turbofan engines, estimating Remaining Useful Life (RUL) using ensemble-trained LSTMs. Combined deep learning with time-series sensor data to forecast component degradation, optimizing maintenance cycles and reducing downtime.
YEAR
2021
DOMAIN
Machine Learning
Predictive Maintenance
Prognostics
TECH STACK
Python
TensorFlow
About the project
Designed a hybrid prognostics system integrating neural networks and probabilistic models to predict failure timelines of industrial engines. Trained on thousands of real-world operational cycles, the system leveraged raw sensor data and statistical inference to deliver high-accuracy, cycle-aware RUL estimations for critical aerospace components.
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Predictive Modelling of Turbofan Engines
OVERVIEW
Engineered a predictive maintenance model for turbofan engines, estimating Remaining Useful Life (RUL) using ensemble-trained LSTMs. Combined deep learning with time-series sensor data to forecast component degradation, optimizing maintenance cycles and reducing downtime.
YEAR
2021
DOMAIN
Machine Learning
Predictive Maintenance
Prognostics
TECH STACK
Python
TensorFlow
About the project
Designed a hybrid prognostics system integrating neural networks and probabilistic models to predict failure timelines of industrial engines. Trained on thousands of real-world operational cycles, the system leveraged raw sensor data and statistical inference to deliver high-accuracy, cycle-aware RUL estimations for critical aerospace components.
Smooth Scroll
This will hide itself!
This will hide itself!