FORMATION IA
Bootcamp Praticien en Maintenance Prédictive
Construisez et déployez des modèles ML capables de prédire les pannes machines avant qu'elles surviennent.
Ce qu'elle couvre
Ce bootcamp intensif dote les ingénieurs de fiabilité et les responsables opérationnels industriels de toutes les compétences nécessaires à la maintenance prédictive : ingestion de données capteurs, feature engineering sur séries temporelles, détection d'anomalies, modèles de temps avant défaillance, et déploiement en production sur edge ou cloud. Les participants travaillent sur des jeux de données industriels réels avec Python, scikit-learn, tsfresh et ONNX. La formation alterne ateliers pratiques (60 %) et sessions magistrales (40 %), pour aboutir à un projet capstone livrant un pipeline PdM déployable. Les participants repartent avec des modèles de code réutilisables, une checklist de gouvernance des modèles et un cadre de décision edge vs. cloud.
À l'issue, vous saurez
- Ingest, align, and engineer features from multi-channel sensor time-series data using Python and tsfresh
- Train, evaluate, and tune anomaly detection models suitable for industrial failure signals
- Build a Remaining Useful Life regression model and interpret its outputs in operational terms
- Make an informed edge-vs-cloud deployment decision and export a trained model to ONNX for edge inference
- Design a retraining and data-drift monitoring strategy to keep PdM models accurate in production
Sujets abordés
- Sensor data acquisition, cleaning, and time-series alignment
- Feature engineering for temporal industrial data (tsfresh, manual crafting)
- Anomaly detection techniques: Isolation Forest, Autoencoders, statistical control charts
- Remaining Useful Life (RUL) and time-to-failure regression models
- Model evaluation metrics specific to maintenance contexts (false alarm rate, detection lead time)
- Edge vs. cloud deployment trade-offs and ONNX model portability
- MLOps fundamentals for PdM: retraining triggers, data drift monitoring
- OEE (Overall Equipment Effectiveness) impact measurement and ROI framing
Modalité
Delivered over 4-5 consecutive days, either on-site at the client's facility (preferred for access to real sensor data) or remotely via a virtual lab environment with pre-loaded industrial datasets (NASA CMAPSS, PHM Society benchmarks). Participants need a laptop with Python 3.10+ and Docker. Roughly 60% of time is spent in hands-on coding labs; 40% in instructor-led sessions and group design reviews. A shared GitHub repository with starter notebooks is provided. Optional half-day follow-up session available 4 weeks post-bootcamp for deployment troubleshooting.
Ce qui fait que ça marche
- Involving both data engineers and maintenance technicians in the bootcamp to close the domain knowledge gap
- Using the organisation's own historical sensor data (even a small subset) for the capstone project
- Establishing a model owner role in operations who monitors alert performance and triggers retraining
- Defining business KPIs (unplanned downtime hours, maintenance cost per unit) before model development begins
Erreurs fréquentes
- Training models on clean benchmark data and discovering the approach fails on noisy real plant signals
- Ignoring class imbalance — failures are rare events, so naive accuracy metrics mask poor recall
- Deploying a cloud-only solution on a factory floor with unreliable connectivity, causing critical latency
- Skipping data drift monitoring, so models degrade silently after equipment upgrades or seasonal changes
Quand NE PAS suivre cette formation
This bootcamp is not the right fit for a team that has no historian or SCADA data pipeline in place — without accessible sensor data, participants cannot complete the capstone and will lack the infrastructure to apply skills post-training.
Fournisseurs à considérer
Sources
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