Predictive maintenance in manufacturing uses Machine Learning to predict when a machine is likely to fail, enabling just-in-time maintenance to prevent downtime without unnecessary inspections.
Predictive maintenance leverages ML to forecast when a machine is likely to fail, enabling maintenance to be performed just in time to prevent downtime without engaging in unnecessary checks.
Sensor Data Aggregation: Collecting and integrating sensor data from machinery, such as temperature, vibration, and acoustics.
Data Processing: Standardizing time-series data from sensors and aligning them with maintenance records.
Feature Engineering: Creating features that capture historical trends and failure patterns.
Model Development: Utilizing regression models or survival analysis to predict the time until failure or classify the equipment condition.
Model Validation: Testing models against historical failures to ensure they can predict future failures accurately.
Model Deployment: Using containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes) to deploy models at scale across various machines.
Performance Monitoring: Tracking model predictions in real-time and comparing them with actual machine performance to detect model drift.
Automated Retraining: Setting up pipelines for retraining models with new data regularly or when model performance drops.