Total Productive Maintenance on Industrial Robots
Predictive maintenance is a crucial component in managing industrial robots, especially in critical environments such as nuclear fuel replacement. This project focuses on developing predictive models using advanced data analytics and machine learning techniques. The goal is to optimize robot fleet management and extend their operational lifespan.
MACHINE LEARNINGDEEP LEARNINGSTREAMLIT APPTOTAL PRODUCTIVE MAINTENANCE
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Main Features
Data Exploration and Feature Engineering: Identifying and transforming key variables for effective modeling.
Remaining Useful Life (RUL) Prediction: Leveraging algorithms like LSTM neural networks for machine state forecasting.
Optimized Decision-Making: Implementing decision frameworks to efficiently schedule maintenance tasks.
Survival Analysis: Modeling potential failures under complex conditions.
Stack Technologique
Languages and Tools: Python, Jupyter Notebook.
Libraries: TensorFlow, Scikit-learn.
Applications: RUL prediction, survival analysis, industrial maintenance.
User Interface with Streamlit : Intuitive and interactive interface.