Introduction

Edge AI, or Artificial Intelligence at the edge, is becoming increasingly popular in various industries for its ability to process data closer to the source, reducing latency and improving efficiency. One of the key application areas of Edge AI is predictive maintenance, where sensors and algorithms are used to predict equipment failures before they occur, saving time and costs associated with unexpected breakdowns. This thesis focuses on the implementation of Edge AI for predictive maintenance in industrial settings.

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter Two: Literature Review
2.1 Introduction to Edge AI
2.2 Predictive Maintenance in Industry
2.3 Edge Computing
2.4 Machine Learning Algorithms for Predictive Maintenance
2.5 IoT and Sensor Technologies
2.6 Challenges and Opportunities in Edge AI for Predictive Maintenance
2.7 Case Studies of Edge AI Implementation in Predictive Maintenance
2.8 Comparison of Edge AI vs. Cloud-based AI for Predictive Maintenance
2.9 Industry Trends in Predictive Maintenance
2.10 Future Research Directions

Chapter Three: System Design and Methodology
3.1 System Architecture for Edge AI in Predictive Maintenance
3.2 Data Collection and Preprocessing Techniques
3.3 Feature Selection and Engineering
3.4 Machine Learning Model Selection
3.5 Training and Testing Data Sets
3.6 Performance Evaluation Metrics
3.7 Edge Device Selection and Deployment Strategy
3.8 Communication Protocols and Data Transfer Methods

Chapter Four: System Implementation
4.1 Data Acquisition and Sensor Integration
4.2 Data Processing and Analysis
4.3 Feature Extraction and Selection
4.4 Model Training and Evaluation
4.5 Real-time Monitoring and Alerting System
4.6 Edge Device Configuration and Deployment
4.7 Integration with Existing Maintenance Systems
4.8 Testing and Validation

Chapter Five: Conclusion
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Limitations and Future Work
5.4 Practical Implications and Recommendations
5.5 Conclusion and Final Remarks

Thesis Overview:

The use of Edge AI for predictive maintenance in industrial settings has been a topic of increasing interest in recent years. This thesis aims to explore the benefits, challenges, and implementation strategies of Edge AI in predictive maintenance.

The first chapter provides an introduction to the topic, including the background of study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms.

The second chapter presents a comprehensive literature review on Edge AI, predictive maintenance, edge computing, machine learning algorithms, IoT, challenges, case studies, comparisons with cloud-based AI, industry trends, and future research directions.

Chapter three details the system design and methodology, covering system architecture, data collection, preprocessing, feature selection, machine learning model selection, training, testing, performance evaluation, edge device selection, and communication protocols.

The fourth chapter focuses on the system implementation process, including data acquisition, sensor integration, data processing, analysis, feature extraction, model training, real-time monitoring, alerting, device configuration, deployment, integration, testing, and validation.

Finally, chapter five concludes the thesis with a summary of findings, contributions, limitations, future work, implications, recommendations, and final remarks. Overall, this thesis aims to contribute to the growing body of knowledge on Edge AI for predictive maintenance and provide practical insights for researchers and industry practitioners alike.

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