Module #1 Introduction to Predictive Maintenance Overview of traditional maintenance approaches, benefits of predictive maintenance, and the role of AI
Module #2 AI Fundamentals for Predictive Maintenance Basic concepts of Artificial Intelligence, Machine Learning, and Deep Learning
Module #3 Types of AI in Predictive Maintenance Introduction to machine learning, deep learning, and other AI approaches for predictive maintenance
Module #4 Data Requirements for AI in Predictive Maintenance Data types, sources, and requirements for building effective AI models
Module #5 Sensor and IoT Data for Predictive Maintenance Overview of sensor technologies and IoT data for predictive maintenance
Module #6 Data Preprocessing and Feature Engineering Techniques for data preprocessing, feature extraction, and feature engineering
Module #7 Introduction to Machine Learning for Predictive Maintenance Supervised, unsupervised, and reinforcement learning approaches for predictive maintenance
Module #8 Regression-based Methods for Predictive Maintenance Linear and non-linear regression models for predicting equipment failures
Module #9 Classification-based Methods for Predictive Maintenance Binary and multi-class classification models for fault detection and diagnosis
Module #10 Clustering and Dimensionality Reduction for Predictive Maintenance Unsupervised learning techniques for anomaly detection and feature extraction
Module #11 Deep Learning for Predictive Maintenance Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for predictive maintenance
Module #12 Anomaly Detection and Fault Diagnosis using AI AI-based methods for detecting anomalies and diagnosing faults in equipment
Module #13 Remaining Useful Life (RUL) Prediction using AI AI-based methods for predicting remaining useful life of equipment
Module #14 AI for Predictive Maintenance in Specific Industries Case studies of AI applications in predictive maintenance for industries such as manufacturing, energy, and transportation
Module #15 Deploying and Integrating AI Models for Predictive Maintenance Deploying AI models in production environments and integrating with existing maintenance systems
Module #16 Evaluating and Improving AI Models for Predictive Maintenance Performance metrics and techniques for evaluating and improving AI models
Module #17 AI Ethics and Explainability in Predictive Maintenance Ethical considerations and techniques for Explainable AI (XAI) in predictive maintenance
Module #18 Best Practices and Future Directions in AI for Predictive Maintenance Best practices for implementing AI in predictive maintenance and future directions in the field
Module #19 Case Studies of AI in Predictive Maintenance Real-world case studies of AI applications in predictive maintenance
Module #20 Implementing AI in Predictive Maintenance - A Hands-on Approach Hands-on implementation of AI models for predictive maintenance using popular libraries and tools
Module #21 AI and IoT for Real-time Predictive Maintenance Real-time predictive maintenance using AI and IoT data
Module #22 Digital Twins for Predictive Maintenance Using digital twins for simulating and predicting equipment behavior
Module #23 AI for Supply Chain Optimization in Maintenance Using AI for optimizing supply chain operations in maintenance
Module #24 AI for Root Cause Analysis and Fault Detection Using AI for root cause analysis and fault detection in equipment failures
Module #25 AI for Predictive Maintenance in 5G and Edge Computing Using AI for predictive maintenance in 5G and edge computing environments
Module #26 AI and Human Collaboration in Predictive Maintenance Collaborative approaches between humans and AI in predictive maintenance
Module #27 AI for Predictive Maintenance in Autonomous Systems Using AI for predictive maintenance in autonomous systems such as drones and robots
Module #28 AI for Predictive Maintenance in Cyber-Physical Systems Using AI for predictive maintenance in cyber-physical systems such as smart grids and transportation systems
Module #29 AI for Condition-Based Maintenance Using AI for condition-based maintenance and real-time monitoring
Module #30 Course Wrap-Up & Conclusion Planning next steps in AI in Predictive Maintenance career
