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**Thesis Overview: Predictive maintenance for data center infrastructure using sensor data and machine learning**

Data centers are critical infrastructures that house servers and networking equipment to store, process, and distribute data. As the demand for data processing continues to increase, it is imperative to ensure the reliability and availability of data center infrastructure. Predictive maintenance, which utilizes sensor data and machine learning algorithms, offers a proactive approach to maintaining data center equipment by predicting failures before they occur.

This thesis aims to explore the use of predictive maintenance for data center infrastructure using sensor data and machine learning. The research will investigate the effectiveness of predictive maintenance in improving the reliability and availability of data center equipment, as well as reducing downtime and maintenance costs. The study will also examine the challenges and limitations of implementing predictive maintenance in data center environments.

Chapter One: Introduction
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 predictive maintenance
2.2 Importance of predictive maintenance in data centers
2.3 Sensors and data collection in data center infrastructure
2.4 Machine learning algorithms for predictive maintenance
2.5 Case studies on predictive maintenance in data centers
2.6 Challenges and limitations of predictive maintenance
2.7 Best practices for implementing predictive maintenance
2.8 Future trends in predictive maintenance for data centers
2.9 Summary of literature review
2.10 Gaps in the existing research

Chapter Three: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Selection of sensors
3.5 Selection of machine learning algorithms
3.6 Experimental setup
3.7 Evaluation metrics
3.8 Ethical considerations

Chapter Four: Discussion of Findings
4.1 Analysis of sensor data
4.2 Performance of machine learning algorithms
4.3 Prediction accuracy of maintenance tasks
4.4 Comparison with traditional maintenance approaches
4.5 Cost-benefit analysis
4.6 Recommendations for implementation
4.7 Areas for future research

Chapter Five: Conclusion and Summary
5.1 Summary of key findings
5.2 Implications for data center operators
5.3 Contributions to the field
5.4 Limitations of the study
5.5 Recommendations for future research
5.6 Conclusion

This thesis will contribute to the body of knowledge on predictive maintenance for data center infrastructure using sensor data and machine learning. By utilizing a proactive approach to maintenance, data center operators can improve the reliability and availability of their equipment, ultimately leading to cost savings and increased operational efficiency.

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