Predicting Failures with Machine Learning

Übersicht

Engineers are tasked with minimizing downtime and enhancing operational reliability. Predictive maintenance has become a powerful way to predict equipment failures before they occur, leading to increased efficiency and cost savings.

With operational efficiency being crucial to competitiveness, companies are seeking engineers with skills in applying predictive maintenance techniques. This course provides engineers with artificial intelligence (AI) skills to proactively identify potential equipment failures, thereby boosting efficiency and reducing operational costs.

Lehrplan

• 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

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Unterrichtet von

Kathy Tao, Rohit Ramanathan, Marissa D'Alonzo, Brian Buechel and Megan Thompson

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Fachgebiete

Computer Science