course | Predictive Maintenance with Data Science Tools and Big Data Analytics

Module 1: Foundations of Predictive Maintenance and Data Handling
 

Introduction to Predictive and Proactive Maintenance
 

• Maintenance strategies: reactive, preventive, condition-based, predictive
• The business value of predictive maintenance
• Data-driven maintenance decision-making
   

Data Collection and Management
 

• Overview of data sources: SCADA, sensors, logs, CMMS
• Data acquisition techniques (e.g., MQTT, OPC-UA, APIs)
• Cleaning and preprocessing maintenance datasets
• Time-series data fundamentals and challenges

Module 2: Foundations of Predictive Maintenance and Data Handling
 

Tools & Platforms Overview
 

• Data science stack: Python, Pandas, Numpy, Scikit-learn
• Big data platforms: Hadoop, Apache Spark, Kafka (intro level)
• Industrial data platforms (OSIsoft PI, Azure IoT, AWS IoT, etc.)

Module 3: Predictive Modeling and Feature Engineering
 

Feature Engineering for Maintenance

• Creating features from time-series data
• Statistical summaries, rolling windows, and lag features
• Domain-specific feature extraction (vibration, temperature, cycles)

Machine Learning for Failure Prediction

• Classification models: Decision Trees, Random Forest, Gradient Boosting
• Regression models for Remaining Useful Life (RUL)
• Model evaluation: confusion matrix, ROC, precision/recall

Anomaly Detection and Advanced Models

• Unsupervised learning for anomaly detection (K-Means, Isolation Forest)
• Predictive maintenance using Deep Learning (e.g., LSTM basics)
• Intro to survival analysis and reliability modeling

Module 4: Strategy, Deployment, and Optimization
 

Scalable Architecture and Data Pipelines
 

• Real-time vs. batch processing
• Building a data pipeline for streaming equipment data
• Integration with CMMS and ERP systems

Module 5: Strategy, Deployment, and Optimization
 

From Model to Maintenance Action
 

• Interpreting model results for operational decisions
• Setting thresholds, alerts, and maintenance triggers
• Building dashboards (Power BI, Grafana, Kibana overview)