Lecture Notes: Predictive Asset Maintenance (PAM) Session
Introduction
- Speaker: Mudit, Founding Member of DeFi
- DeFi: Global AI community with members from 150+ countries
- Offers free AI and data science learning resources
- Engages in AI challenges, interaction within the community
- Today's topic: Predictive Asset Maintenance (PAM)
- Guest Speaker: Divyanshu Vyas
- Founder of "Petroleum From Scratch"
- Data Science Researcher at Shell
- Experience with Accenture, L&T Infotech, and startups
What is Predictive Asset Maintenance?
- Asset: Any equipment or machinery
- Predictive: Involves forecasting potential equipment failures
- Maintenance: Ensuring machinery operates without failure
- Aim: Use data analytics to create proactive maintenance models
- Machine learning models detect abnormal behaviors for early detection of failures
Traditional vs. Predictive Maintenance
- Reactive Maintenance:
- Wait for failure, then repair (e.g., phone chargers)
- Periodic replacement which may lead to unnecessary costs
- Physics-based Condition Monitoring:
- Monitoring data from sensors to detect anomalies
- Based on field rules but limited by data consistency
- Predictive Maintenance (PAM):
- Uses AI/ML for anomaly detection
- Models trained on historical data
- Proactive with real-time data monitoring
Practical Application of PAM
- Commonly used in:
- Control valves
- Electrical Submersible Pumps (ESPs)
- Downstream refineries
- Aircraft engines
- Factors like RPM, temperature, torque are crucial
- Collaboration with field experts necessary for effective implementation
Challenges in PAM Projects
- Data Quality: Must have high-quality sensor data
- Class Imbalance: Failure instances are rare
- Techniques like oversampling or under-sampling needed
- Explainability: Field engineers need clear evidence for model outputs
Hands-on Project Workflow
- Data Exploration:
- Import and visualize sensor data
- Identify and handle null values
- Perform outlier detection
- Feature Engineering:
- Create statistical condition monitoring features (e.g., moving averages)
- Modeling:
- Classification models using RUL-based labels
- Consider class imbalance and interpretability
Key Insights
- Proactive maintenance can prevent costly downtimes
- Must balance between accurate predictions and unnecessary alarms
- Models should be explainable to ensure trust and effectiveness
Tools and Techniques
- Dash and Streamlit for creating live dashboards
- Use of standard deviation, skewness for feature engineering
- Importance of physics understanding in data interpretation
Conclusion
- Predictive maintenance is a blend of engineering, data science, and domain expertise
- Aim for efficient collaboration between data scientists and field engineers
Additional Information
- Upcoming DeFi bootcamps for intensive learning
- Continuous engagement in the community for growth
Note: This summary focuses on understanding PAM, its challenges, and methodologies. It encapsulates hands-on aspects along with theoretical backing for a comprehensive learning experience.