Predictive Maintenance Model (Concept)
Time-series machine learning prototype for early detection of equipment anomalies.
Context / Problem
Industrial equipment generates continuous sensor data, yet failures are often detected reactively. The concept investigates how anomaly detection models can provide early warnings.
Approach
Used synthetic time-series data to simulate vibration and temperature patterns. Implemented baseline models (Isolation Forest, LSTM-based anomaly detection) and compared performance.
Model Strategy
Emphasized interpretability and deployment simplicity over raw complexity. Focused on thresholding strategies and false-positive control for operational usability.
Outcome
Validated that early anomaly signals can be detected before critical thresholds. Highlighted the importance of domain calibration and monitoring in production.