aiInternal Project
Machine Stop/Fail Prediction
Predictive maintenance model for identifying scrap products using sensor data with 95% accuracy.
Developed at ProManage
Lead Data Scientist
2019-2020
Internal project - no public access
Screenshots
About This Project
Built a predictive model for manufacturing environments that identifies potential machine failures and scrap products using sensor data analysis. The system processes time-series sensor data with over-sampling techniques for handling unbalanced data, enabling proactive maintenance scheduling and reducing downtime.
Key Features
- Time-series sensor data analysis and pattern recognition
- Over-sampling techniques for unbalanced data handling
- Predictive alerts for potential machine failures
- Scrap product identification before quality check stage
- Historical trend analysis and failure pattern detection
- Integration with manufacturing execution systems
Challenges & Solutions
- Processing high-frequency sensor data streams efficiently
- Handling heavily unbalanced time-series datasets
- Building models that generalize across different machine types
- Reducing false alarms while maintaining high recall
Results & Impact
- 95% accuracy in scrap product identification
- 20% improvement in client investment returns
- Significant reduction in unplanned machine downtime
Technologies Used
PythonScikit-LearnKerasNumPyPandasDockerSQL
Project Details
- Category
- ai
- My Role
- Lead Data Scientist
- Duration
- 6 months
- Year
- 2019-2020
- Company
- ProManage
- Status
- Internal / Private
Internal Project
This project was developed for internal use at ProManage. Source code and live demo are not publicly available due to confidentiality.