Collection of various predictive maintenance solutions, showcasing different approaches and techniques to anticipate and prevent equipment failures.
Real-time plant performance monitoring solution featuring:
- Equipment effectiveness (OEE) calculation
- Multi-factory performance tracking
- Real-time sensor data processing
- Production KPI visualization
Real-time monitoring and anomaly detection solution featuring:
- Synthetic data generation
- Decision tree classifier training
- Real-time inference and scoring
- Anomaly visualization dashboard
Predictive maintenance solution leveraging synthetic datasets and machine learning models like Random Forest, Decision Tree, and Stochastic Gradient Boosting. Features include:
- Failure prediction for CNC machines
- Data preparation and model training
- Root cause analysis reporting
- Scalable solutions for IoT-scale datasets on Databricks
A predictive maintenance solution for accurately estimating an engine’s Remaining Useful Life (RUL). Features include:
- Simulation of real-world operational conditions
- Critical parameter analysis (e.g., air temperature, torque, tool wear)
- Advanced machine learning algorithms:
- Random Forest
- Decision Tree
- Stochastic Gradient Boosting
- High-accuracy failure predictions to optimize maintenance schedules and reduce downtime.