Derrick Fongang Fongang

Predictive maintenance to detect and prevent anomalies on the machines

• Collecting production data: torque pressure, pump pressure and rotating speed.

• Concatenating data on each sensor to create a time series.

• Anomaly detection with the IQR: interquartile range, and the isolation forest techniques.

• Integrating acceleration data and frequence detection for the wireless vibration monitoring system.

• Time series data visualization with Seaborn

Technologies: Python 3.10 – Scikit Learn – Pandas – Numpy – Seaborn – Jupyter Lab – Windows

Analyzing the foundry parameters that impact the quality of produced pieces.

• Detecting features that impact the most the quality of produced pieces.

• Feature importance analysis with Machine Learning algorithms: Random Forest, Logistic Regression, GradientBoosting, Permutation Feature Importance

• Boostrap Sampling to manage the unbalanced dataset

• Data Science Evangelist, Training and Vulgarization

Technologies: Python 3.7 – Scikit Learn – Pandas – Numpy – Data Visualization with Seaborn – Jupyter Lab – Windows OS

Comparing solutions and technologies for the implementation of the Artificial Intelligence Observability.

• Comparative study of different A.I. Obs solutions, such as Elastic Observability.

• Designed and implemented the architecture for collecting system logs from linux servers and windows servers.

• Visualization, storage and analysis of the logs collected for the predictive maintenance of breakdowns on the server warehouse.

Technologies: Python 3.7 – Scikit Learn – Tensorflow – NLP – Docker – Data Visualization with Seaborn – Windows OS – Serveur Linux – Syslogs – Elasticsearch