Overview
Advance your engineering career with predictive maintenance solutions. Acquire AI expertise to anticipate equipment failures, minimize downtime, and boost operational efficiency.
Syllabus
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- Introduction to Predictive Maintenance
-- Importance and benefits of predictive maintenance
-- Overview of predictive maintenance strategies
-- Comparison with reactive and preventive maintenance
- Fundamentals of Machine Learning in Predictive Maintenance
-- Key machine learning concepts and terminology
-- Types of machine learning: supervised, unsupervised, and reinforcement learning
-- Model training and evaluation basics
- Data Collection and Management
-- Identifying relevant data sources for predictive maintenance
-- Data quality assessment and preprocessing
-- Time series data and anomaly detection
- Feature Engineering for Predictive Models
-- Identifying important features in predictive maintenance
-- Techniques for feature extraction and selection
-- Creating and validating datasets
- Building Predictive Maintenance Models
-- Classification vs. regression in failure prediction
-- Overview of common algorithms (e.g., Decision Trees, Random Forest, Neural Networks)
-- Implementing models using Python's scikit-learn and TensorFlow
- Model Evaluation and Optimization
-- Metrics for evaluating predictive maintenance models (e.g., accuracy, precision, recall)
-- Cross-validation techniques
-- Hyperparameter tuning and model optimization
- Implementing Predictive Maintenance Systems
-- Integrating machine learning models into maintenance workflows
-- Real-time monitoring and alert systems
-- Case studies and industry applications
- Challenges and Best Practices
-- Handling imbalanced datasets
-- Dealing with data sparsity and noise
-- Ethical and practical considerations in predictive maintenance
- Emerging Trends and Future Directions
-- The role of IoT in predictive maintenance
-- Advances in AI and machine learning for equipment monitoring
-- Prospects for automated maintenance decision-making
- Capstone Project
-- Developing a complete predictive maintenance solution
-- Applying learned concepts to a real-world scenario
-- Presenting and defending the project findings
Taught by
Kathy Tao, Rohit Ramanathan, Marissa D'Alonzo, Brian Buechel and Megan Thompson
Tags
On-Demand
