École Nationale Supérieure des Mines de Rabat
Engineering Cycle, Electromechanics
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I specialize in turning raw maintenance data—multisensor time-series (vibration, temperature, current), CMMS/SCADA logs, and inspection imagery—into reliable training datasets for predictive maintenance. I design failure taxonomies from FMEA/FMECA, define labeling guidelines, and create ground-truth targets such as health states, failure windows, and Remaining Useful Life (RUL). On the vision side, I annotate and review defect classes (wear, corrosion, leaks, surface anomalies) and build OCR datasets from nameplates/work orders to link events back to assets—producing clean, model-ready data for anomaly detection and prognosis.
Khalid G. hasn’t added any AI Training or Data Labeling experience to their OpenTrain profile yet.
Engineering Cycle, Electromechanics
CPGE (Preparatory Classes), Technologies & Industrial Sciences
Education Fellow-PhD Student
Final-Year Engineering Project